If you’ve ever prepared for a FAANG interview — or any top-tier tech company interview — you already know one truth: cracking the system design round is brutally hard.

Many candidates spend months solving LeetCode problems, mastering algorithms, and brushing up on data structures, only to get completely stumped when faced with an open-ended system design question like:

• “Design a scalable messaging platform like WhatsApp.”

• “How would you design a URL shortening service like Bit.ly?”

• “Build a distributed file storage system.”

Unlike coding challenges with clear inputs and outputs, system design interviews test your ability to architect real-world systems, make trade-offs, and communicate your ideas effectively — all under time pressure.

Here’s the kicker: just knowing concepts isn’t enough. You might understand what caching, sharding, or load balancing is, but can you confidently apply those ideas to design an efficient, scalable system on the fly? That’s where most candidates struggle.

This is exactly the gap that Codemia.io aims to fill. And right now, they’re offering an unbeatable 65% OFF a lifetime plan — which makes this the perfect time to level up your skills.

In this post, I’ll break down:

• What Codemia.io is and how it works

• Why system design interviews are tough (and how Codemia helps)

• A detailed look at Codemia’s features and pricing

• How it complements other popular resources like ByteByteGo

• My honest take on whether the lifetime plan is worth it

Let’s dive in.

Why Are System Design Interviews So Difficult?

System design interviews are unlike any other part of the tech hiring process. Coding challenges can be mastered with repetition, but design questions require a deep understanding of distributed systems, trade-offs, scalability, and communication skills.

Here’s why they’re uniquely tough:

1. Open-ended problems: There’s no single “correct” answer. Interviewers want to see your thought process, ability to handle ambiguity, and skill in balancing trade-offs.

2. Real-world scale: You’re asked to design systems like Netflix, Twitter, or Uber, where billions of requests happen every day. This requires practical knowledge of caching, databases, replication, and more.

3. Communication under pressure: You need to clearly explain your choices, justify trade-offs (e.g., SQL vs NoSQL, monolith vs microservices), and adapt as interviewers push back.

You can read books, watch videos, and learn every concept in theory — but until you practice applying these ideas, you won’t feel confident in an actual interview.

Enter Codemia.io — A Practice-First System Design Platform

Codemia.io is a hands-on, interactive platform designed specifically for System Design Interview preparation. Unlike static courses or books, Codemia focuses on active learning — solving real system design problems and getting feedback to improve.

Here’s what makes it stand out:

• 120+ System Design Problems: Covering everything from social networks and messaging apps to e-commerce platforms and distributed databases.

• 60+ Editorial Solutions: Expert-written explanations so you can compare your answers with best practices.

• Object-Oriented Design Problem Set: Strengthen your fundamental design skills beyond large-scale systems.

• Unlimited Iterative & Interactive AI Learning: Submit solutions, receive AI-powered feedback, and improve in real time.

• Full Feedback History: Track your progress and revisit past solutions to identify growth areas.

• Solution Sharing for Peer Feedback: Learn collaboratively by comparing approaches with other engineers.

• Easy-to-Use UI on Desktop & Mobile: Practice anywhere, anytime.

Instead of just reading about system design, you’ll be actively designing systems, making mistakes, and refining your solutions until you can confidently tackle any question.

Here is the link to learn more: explore codemia.io

The Current Offer — 55% OFF Lifetime Plan

Codemia is currently running a limited-time promotion that gives you 65% off their lifetime plan.

At just $159.20 one-time, the lifetime plan costs less than a single coding bootcamp course but gives you permanent access to a constantly updated system design library. Considering that system design skills remain relevant for your entire career, this is a bargain.

👉 Grab Codemia Lifetime Plan — 55% OFF Here

Why does Codemia.io complement other resources?

Many engineers preparing for system design interviews start with popular platforms like:

• ByteByteGo (for theory and visual explanations)

• Grokking the System Design Interview

• Books like Designing Data-Intensive Applications by Martin Kleppmann

These are all excellent resources for learning concepts — but concepts alone won’t get you hired. You need to apply what you’ve learned to open-ended problems and refine your answers with feedback.

This is where Codemia shines:

• ByteByteGo is fantastic for learning theory.

• Codemia is perfect for practicing applications.

• Using both gives you the best of both worlds.

If you’re already using ByteByteGo or planning to get their lifetime plan, Codemia is the ideal companion to make sure you can apply those concepts in an interview setting.

How does Codemia help with FAANG Interview Prep?

FAANG companies (Facebook/Meta, Amazon, Apple, Netflix, Google) are known for their rigorous hiring process. System design rounds are typically 45–60 minutes and focus on evaluating:

• Your ability to analyze ambiguous requirements

• How well you can design scalable systems under real-world constraints

• Your communication and trade-off analysis skills

Codemia’s interactive, iterative approach helps you:

1. Develop a repeatable framework for tackling any design problem.

2. Practice structuring your answers to handle follow-up questions.

3. Build confidence by solving FAANG-level problems before the real interview.

This isn’t just about passing interviews — it’s about becoming a better engineer who can architect scalable, reliable systems.

Lifetime Plan vs. Annual Plan — Which One Should You Pick?

Let’s be honest: the annual plan is attractive if you’re on a budget or have interviews scheduled soon. But if you’re serious about a long-term career in software engineering, the lifetime plan is the smart investment.

Here’s why:

• Cost Efficiency: The lifetime plan is roughly the cost of just 3 years of the annual plan. If you plan to revisit system design (and you will), it pays for itself.

• No Renewal Hassles: Interviews don’t stop after one job. Promotions, career switches, or new roles will require system design prep again and again.

• Lifetime Updates: Codemia continuously adds new problems, editorial solutions, and features — all included in the lifetime plan at no extra cost.

If you ask me, I highly recommend that you take the lifetime plan and set it for life. The discount is also very good to lock in before the price changes.

Here is the link to learn more — Codemia.io annual and lifetime plan

Tips to Maximize Codemia.io

To get the most out of Codemia:

1. Start with Fundamentals: Tackle basic problems to build confidence before moving to FAANG-level challenges.

2. Use the AI Feedback: Don’t just glance at solutions — read feedback carefully and revise.

3. Simulate Real Interviews: Set a 45-minute timer and explain your design out loud as if an interviewer is listening.

4. Review Past Attempts: Revisit old solutions to track growth and identify recurring mistakes.

How Codemia Fits into a Complete Interview Strategy

For best results, combine Codemia with:

• LeetCode / HackerRank for coding rounds

• ByteByteGo or Designing Data-Intensive Applications for conceptual understanding

• Behavioral Prep (e.g., STAR method) for HR interviews

This three-pronged strategy — coding + concepts + practice — will give you a huge edge over other candidates.

Final Thoughts — Is Codemia.io Worth It?

If you’re serious about cracking FAANG system design interviews, Codemia.io is absolutely worth it — especially at the current 65% off lifetime deal.

System design isn’t just about memorizing patterns; it’s about thinking critically, making trade-offs, and communicating effectively. Codemia gives you the hands-on practice you need to master those skills, while the lifetime plan ensures you’ll always have access to new problems as your career evolves.

For just $159.20 one-time, you’re not just buying a prep tool — you’re investing in a resource that will support you every time you face a new system design challenge, whether it’s for a new job, a promotion, or a personal project.

👉 Grab Codemia Lifetime Plan — Save $299.80 Today

This is one of those rare investments that pays off every time you interview — and every time you build systems in the real world.

All the best for your System design and OOP Design Interviews. If you have any doubts or questions, feel free to ask in the comments.

P. S. — If you just want to do one thing at this time then I suggest you to join Codemia.io and start practicing system design problems. The best way to learn is by doing and learn when you get stuck.

Hello folks, Design patterns are an essential topic for object-oriented programmers, like Java and C++ developers. It becomes even more important as your experience grows because everybody starts expecting a lot more from you in terms of writing quality code.

I often receive emails from intermediate and senior Java developers about progressing to the next level, like how to become a Tech Lead, Team Lead, or software architect; knowledge of design patterns plays an important role in taking more technical responsibility.

While the legendary GoF book is the bible for object-oriented design patterns, in my opinion, it is a bit too dry and academic.

Many of you might have already read it, but I doubt you have enjoyed it a lot. The first real book I read on the design pattern was the Head-First Design Pattern book.

It’s full of exciting stories, cartoons, UML diagrams, fireside chats to analyze the pros and cons of each design pattern, fill in the blanks, exercises, and most importantly, some non-trivial code examples.

Another good thing is that instead of focusing on all the patterns, it focuses on a good set of core patterns, like Decorator, Observer, Factory, Command, Strategy, and Facade patterns.

I also found that the Head First book is far accessible than the GoF book. It’s a great book and quite informative, but it’s not the best read. Since now I have read several books on design patterns, I kind of know which are the best books and why and this list is a collection of that experience.

You can choose one, two, or a couple of books to refresh your knowledge on design patterns or start learning them from scratch.

I have also included books on Microservices design patterns because Microservices is now the most popular architecture for Java applications, and I think every Java developer should know Microservices patterns.

6 Best Microservices and OOP Design Patterns Books for Java Programmers in 2026

So, what are we waiting for? Here is my collection of some of the best books to learn object-oriented design patterns and general design patterns for Java developers.

1. Head First Design Patterns 2nd Edition

This is probably the best book to learn Design Patterns, if you are a Java developer with 2 to 3 years of experience and have never heard of the design pattern.

You can start learning and making sense of them after reading this book. It introduces a design pattern by first explaining the problem and how the design pattern solves the problem. It is not academic and, instead, much more readable.

You will learn about some of the most important design patterns, like the Decorator, Command pattern, Facade pattern, and Observer pattern.

The book is now celebrating its 10th Anniversary, first launched in 2004 and now also updated for Java 8. If you want to purchase just one book to learn the design pattern, then this should be the one.

And I am also glad to tell you that the much-awaited second edition of this book is now also available, which will teach you how to code design patterns using modern Java as well as many design principles that are essential to create extensible and maintainable object-oriented software.

Here is the link to get this book — Head First Design Patterns 2nd Edition

The only problem with this book is that it doesn’t cover all the design patterns.

So, if you want to learn more patterns after reading this book, I suggest you take a look at this Design Pattern in Java course on Udemy by Dmitri Nestruk, who will teach you the modern implementation of classic design patterns in Java.

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2. Microservices Patterns: With examples in Java by Chris Richardson

This book dives deep into microservices patterns, providing practical examples using Java. It’s also one of the most recommended books on Microservices patterns and one of the must-reads for Java developers.

It covers topics such as service discovery, Event Sourcing, CQRS, SAGA, Database Per Microservices, API Gateway, Circuit-Breaker, event-driven architecture, data management, and more.

Here is the link to get this book — Microservices Patterns: With examples in Java

And, if you want, you can also combine this book with an online course like Grokking Microservices Design Patterns from Designgurus.io, one of my favorite websites for learning System Design and Microservices. This course nicely complements the book.

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3. Design Patterns: Elements of Reusable Object-Oriented Software

This is the classic GOF design pattern book, which is referred to everywhere. This is the first book that compiled the list of 24 object-oriented patterns and instantly got popular.

This can be an excellent reference book for a design pattern, as it covers all of them and probably in the most detailed way.

The only thing I didn’t like about this book is that it’s not very readable, which means you will get tired after reading a couple of pages.

On the other hand, you can read many chapters in one go in the previous book, like Head First Design Patterns.

Here is the link to get this book — Design Patterns: Elements of Reusable Object-Oriented Software

In case you have some trouble understanding the text, you can combine this book with the From 0 to 1: Design Patterns — 24 That Matter — In Java course from Udemy, which also covers all of these design patterns.

After joining that course and reading this book, my understanding of design patterns improved significantly.

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4. Head First Object-Oriented Analysis and Design

This is another excellent head-first book on object-oriented analysis and design from the Head First series, and one of my all-time favorites. If you can read this book before reading the Head First design pattern.

This will teach you OOP basics like why composition is better than Inheritance and why you should program for interfaces rather than implementation.

You should actually read this book before reading any book on Design patterns because it will teach you object-oriented basics, which is the core of any design principle and patterns.

If you have a good idea of what a class, object, function, and how to use Abstraction, Inheritance, Polymorphism, and Encapsulation, you can understand any pattern.

In order to get the best result, I also suggest you combine this book with the Design Patterns by Kenny Wong on Coursera. It’s very comprehensive and provides interactive, real-world examples to understand many more design patterns.

I have personally tried that it works really great. The course also provides a lot of practice material that you can use to reinforce the concepts you learned in the book and vice versa.

Here is the link to get this book — Head First Object-Oriented Analysis and Design

If you want to have a quick look at design principles, you can also check my post about 10 essential Object-oriented principles for programmers here.

5. UML for Java Programmers By Uncle Bob Martin

The UML is an essential skill for any serious Object-oriented programmer, particularly for Java programmers. Why? Because one picture is worth of thousand words, and UML lets you convey the design, which is in your mind to all team members.

When you use UML to design your system, you will also identify gaps and issues in your design early, and you can address them without rewriting your code. It also helps you to develop design skills and improve your clarity of thinking.

The UML for Java programmers by Uncle Bob is what you need to know about UML. It also comes with some design problems, like designing a coffee machine, similar to our Vending machine design problem.

Here is the link to get this book — UML for Java Programmers

If you need a course, you can also join the Software Design and Architecture Specialization on Coursera.

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6. Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith

Tearing down a monolith and migrating to microservices is one of the hardest architectural challenges. Sam Newman, one of the top experts in microservices, lays out a practical, incremental approach to make the transition without breaking your system.

What You’ll Learn:

1. When and why you should (or shouldn’t) break a monolith into microservices.

2. Step-by-step migration strategies, including strangler patterns and incremental refactoring.

3. How to handle data consistency, transactions, and API versioning.

4. Techniques for managing microservices in production, including deployment and monitoring.

If you’re working with legacy applications or thinking about modernizing your architecture, this book is essential reading.

Here is the link to get this book — Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith

And, if you want, you can also combine this book with the Microservices Architecture — The Complete Guide (Udemy) course; it’s a really great course and affordable too.

That’s all about the best books to learn object-oriented and Microservices design patterns in Java in 2026. If you’re looking to learn them, nothing’s better than Head First Design Patterns — it’s not such an excellent reference text, though.

I wouldn’t get too hung up on design patterns. As with most of the good ones, you’ll write anyhow if you’re a good software developer.

Bad developers may follow the book and turn simple problems into disaster areas. In the era of online courses, you can also combine these books with Design Patterns in Java, one of my favorite courses to learn patterns. He really explains patterns well.

One book that is not included in this list because I found it later is Design Patterns in Java by Steven Metsker and William C. Wake. It’s an awesome book that teaches you design patterns with the learn-by-doing method.

No book like this presents you with scenarios and asks how you would solve them using a particular design pattern. If you really want to master the design pattern, I highly recommend this book as well.

By the way, if you are a senior developer who already knows design patterns, then you can also check out the Head First Software Architecture book, which is a great read for experienced developers like you. It’s like going one step ahead from design patterns to software architecture.

P.S. — If you want to learn Microservices and are looking for resources, then you can also check out these Microservices resources; they are the best ones you can find online.

Hello everyone,

Happy Independence Day! 🇺🇸

One of my favorite things about this time of the year isn’t just the celebrations—it’s the incredible discounts on developer learning platforms, coding interview resources, and system design courses.

I actually wait for these sales every year because they’re the best opportunity to invest in your career without paying full price. This year, I picked up a few subscriptions myself, including ByteByteGo, Codemia, Exponent, and AlgoMonster, along with several Udemy courses that I’ll be working through over the coming months.

If you’ve been thinking about preparing for coding interviews, mastering system design, learning AI, or becoming a better backend engineer, this is the perfect time.

Here are my favorite deals.

ByteByteGo — 50% + 30% OFF

If you’re preparing for System Design interviews, this is my #1 recommendation. ByteByteGo explains complex distributed systems through simple diagrams and practical examples, making it much easier to understand topics such as caching, sharding, load balancing, and distributed databases.

Udacity Nanodegrees — 50% OFF

Looking to learn AI, Cloud Computing, Data Engineering, or Autonomous Systems? Udacity’s Nanodegree programs are among the most comprehensive online and include real-world projects that strengthen your portfolio.

Codemia Lifetime — 60% OFF

One of the fastest-growing platforms for System Design interview preparation. It offers interactive design problems, mock interviews, and detailed walkthroughs from experienced engineers.

Exponent — 70% OFF

Excellent resource for FAANG interview preparation, with mock interviews, behavioral interview guidance, and coding interview practice.

Educative Premium Plus —65% OFF

Interactive text-based courses covering System Design, Java, Python, AI, DevOps, and much more—perfect if you prefer learning by reading and coding instead of watching videos.

DesignGurus — 55% OFF

One of the best platforms for coding interviews and System Design preparation, featuring curated interview courses and realistic practice questions.

Everything Java Interview Bundle — 50% OFF

Ideal for Java developers preparing for technical interviews, covering Java fundamentals, Spring Boot, multithreading, collections, JVM, and more.

101 Blockchain — 50% OFF

A great place to learn Blockchain, Web3, Enterprise Blockchain, and emerging decentralized technologies.

Javarevisited Substack — 35% OFF

Get access to premium articles covering Java, Spring Boot, System Design, SQL, Microservices, career advice, and coding interview preparation. New premium content is added regularly.

Coursera Plus — 40% OFF

Unlimited access to thousands of courses and Professional Certificates from Google, IBM, Meta, Stanford, and many other leading institutions.

DataCamp — 10% OFF

One of the best platforms for learning Python, SQL, Data Analysis, Data Science, and Machine Learning through interactive coding exercises.

AlgoMonster Lifetime — 50% OFF

My favorite platform for learning coding interviews uses patterns instead of memorizing solutions. Excellent for LeetCode preparation.

Udemy — Up to 80% OFF

Thousands of affordable courses covering Java, Spring Boot, Kubernetes, AI, System Design, Cloud Computing, and virtually every programming topic.

ZTM Academy — 20% OFF

Project-based courses on Web Development, AI, Machine Learning, Python, Cloud Computing, and software engineering.

Frontend Masters

Premium video courses taught by industry experts on JavaScript, React, TypeScript, Node.js, performance optimization, and modern frontend development.

Bugfree.ai — 25% OFF

A useful AI-powered platform for interview preparation, helping you practice coding and technical interviews more effectively.

If You Only Buy One...

If you’re only planning to invest in one resource this year, I’d recommend ByteByteGo.

It isn’t the cheapest platform, which makes this one of the best opportunities to grab a lifetime subscription at a significant discount. If System Design interviews are on your roadmap, it’s an investment you’ll likely use for years.

Whichever platform you choose, pick something that helps you move toward your next goal—whether that’s landing a better job, mastering system design, learning AI, or becoming a stronger software engineer.

These discounts won’t last forever, so if you’ve been waiting for the right time to invest in yourself, this is it.

Happy learning, and Happy Independence Day!

Architectural patterns provide proven solutions to common problems encountered in distributed systems, ensuring reliability, scalability, and maintainability.

Among these patterns, some stand out as fundamental for managing data and communication flow effectively, which we will see in this article.

These are also important topics for System design interviews, and knowledge of these patterns goes a long way in solving System design problems and impressing your interviewer.

Apart from preparing common System design questions like API Gateway vs load balancer, Forward Proxy vs Reverse Proxy, as well as common System Design problems, it makes sense to know about these patterns as well.

Let’s find out more about these patterns to understand their principles and applications.

By the way, if you are preparing for System design interviews and want to learn System Design in depth, then you can also check out sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io, and Udemy, which have many great System design courses

Also, a solid knowledge of various Architecture patterns like Peer to Peer Pattern, API Gateway goes a long way in designing systems which which can withstand the test of time in production. On that note, here is a nice diagram from DesignGuru.io on Microservices architecture:

9 Best Architectural Patterns for Distributed Systems

In the past, you have learned about essential Microservice design patterns like Event Sourcing, SAGA, Database Per Microservices, API Gateway, Circuit-Breaker also shared best practices to design Microservices. Now it’s time to see a brief overview of common architecture patterns for Data communication.

1. Peer-to-Peer (P2P) Pattern

The Peer-to-Peer pattern fosters direct communication between two or more components without the need for a central coordinator.

In this decentralized model, each node in the network can act as both a client and a server, enabling efficient resource sharing and collaboration.

P2P architectures are commonly used in file sharing systems, decentralized applications (DApps), and blockchain networks, where resilience and scalability are paramount.

Here is what P2P architecture looks like:

2. API Gateway Pattern

An API Gateway serves as a unified entry point for client requests to access backend services within an application.

By consolidating multiple APIs into a single interface, it simplifies client-server interactions and enforces security, authentication, and rate-limiting policies.

API Gateways are essential components in microservices architectures, enabling service discovery, load balancing, and protocol translation while abstracting the complexities of backend systems.

Here is how it looks:

If you like to watch, here is another great video from ByteByteGo, which explains API Gateway

3. Pub-Sub (Publish-Subscribe)

The Pub-Sub pattern decouples message producers (publishers) from consumers (subscribers) through a message broker or event bus like Kafka, Solace, RabbitMQ, or ActiveMQ.

Publishers broadcast messages to predefined topics or channels, while subscribers express interest in specific topics and receive relevant messages asynchronously.

Pub-Sub architectures facilitate loose coupling, scalability, and fault tolerance, making them ideal for real-time messaging systems, event-driven microservices, and IoT platforms.

Here is how the Pub-sub pattern looks:

4. Request-Response Pattern

The Request-Response pattern represents the fundamental interaction model in distributed systems, where a client sends a request to a server and awaits a corresponding response.

This synchronous communication paradigm is prevalent in web applications, RESTful APIs, and RPC (Remote Procedure Call) frameworks.

Request-Response interactions ensure predictable behavior and enable error handling, making them suitable for transactional workflows and user-facing interfaces.

Here is how the Request-Response model looks in action:

5. Event Sourcing Pattern

Event Sourcing is a distributed system pattern for persisting the state of an application as a sequence of immutable events.

Instead of storing the current state directly, events representing state transitions are stored and replayed to reconstruct the application state when needed.

Event Sourcing enables auditability, temporal querying, and replayability, making it well-suited for financial systems, collaborative editing tools, and domain-driven designs where historical data is crucial.

Here is how an Event Sourcing pattern looks:

And, if you like to watch, here is a nice video on Event Sourcing, which is worth watching:

6. ETL (Extract, Transform, Load) Pattern

ETL is a data integration pattern used to extract data from multiple sources, transform it into a standardized format, and load it into a destination database or data warehouse.

This pattern is essential for data migration, synchronization, and consolidation tasks in business intelligence, data analytics, and data warehousing projects.

ETL pipelines automate data workflows, handle data quality issues, and support batch processing of large datasets.

Here is how ETL looks in action:

7. Batching Pattern

Batching involves accumulating data over a period or until a certain threshold is reached before processing it as a single unit.

Aggregating multiple operations into larger batches reduces overhead and improves efficiency in data processing pipelines.

Batching is commonly employed in data ingestion, ETL processes, and distributed computing frameworks to optimize resource utilization and minimize latency.

Here is what a Batching pattern looks like:

9. Streaming Processing Pattern

Streaming Processing enables the continuous ingestion, processing, and analysis of data streams in real-time. Unlike batch processing, which operates on static datasets, streaming systems handle infinite data streams with low latency and high throughput.

Streaming architectures support event-driven processing, complex event processing (CEP), and real-time analytics applications in domains such as finance, IoT, and cybersecurity.

Here is a nice diagram from Hazlecast which shows Stream Processing in action:

10. Orchestration Pattern

Orchestration involves a central coordinator (an orchestrator) managing the interactions between distributed components or services to execute a workflow or business process.

By coordinating task execution, handling exceptions, and enforcing dependencies, orchestration ensures the orderly execution of complex workflows spanning multiple systems.

Orchestration engines are used in workflow automation, business process management (BPM), and microservices orchestration to streamline operations and improve agility.

Here is how it looks by using the Saga Orchestrator Pattern

And, here is a nice diagram from ByteByteGo which explains all these architecture styles in a more visual way

Best System Design Interview Resources

Here is a curated list of the best system design books, online courses, and practice websites, which you can check to better prepare for System design interviews. Most of these courses also answer questions I have shared here.

1. DesignGuru’s Grokking System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

2. Codemia.io: This is another great platform to practice System design problems for interviews. It has more than 120+ System design problems, many of which are free, and also a proper structure to solve them.

3. “System Design Interview” by Alex Xu: This book provides an in-depth exploration of system design concepts, strategies, and interview preparation tips.

4. “Designing Data-Intensive Applications” by Martin Kleppmann: A comprehensive guide that covers the principles and practices for designing scalable and reliable systems.

5. LeetCode System Design Tag: LeetCode is a popular platform for technical interview preparation. The System Design tag on LeetCode includes a variety of questions to practice.

6. “System Design Primer” on GitHub: A curated list of resources, including articles, books, and videos, to help you prepare for system design interviews.

7. Educative’s System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

8. High Scalability Blog: A blog that features articles and case studies on the architecture of high-traffic websites and scalable systems.

9. YouTube Channels: Check out channels like “Gaurav Sen” and “Tech Dummies” for insightful videos on system design concepts and interview preparation.

10. ByteByteGo: A live book and course by Alex Xu for System design interview preparation. It contains all the content of the System Design Interview book volumes 1 and 2, and will be updated with volume 3, which is coming soon.

11. Exponent: A specialized site for interview prep, especially for MAANG companies like Amazon and Google. They also have a great system design course and many other materials that can help you crack FAANG interviews.

image_credit - ByteByteGo

You should also combine theoretical knowledge with practical application by working on real-world projects and participating in mock interviews. Continuous practice and learning will undoubtedly enhance your proficiency in system design interviews.

That’s all about the 9 essential Software architecture patterns. Most of these patterns are also applicable to distributed systems, and they are also quite important for system design interviews.

In short, the effective management of data and communication flow is critical for building robust and scalable distributed systems.

Architectural patterns such as Peer-to-Peer, API Gateway, Pub-Sub, Request-Response, Event Sourcing, ETL, Batching, Streaming Processing, and Orchestration offer valuable solutions to address diverse challenges in system design and implementation.

By understanding these software architecture and distributed system patterns and their respective strengths and trade-offs, architects and developers can make informed decisions to design systems that meet the evolving needs of their applications and users.

Hello folks, if you are preparing for a Java and Spring Developer interview, then you may have come across the “difference between @Controller and @RestController annotations” in Spring Boot and Spring Framework in general.

In Spring MVC, both @Controller and @RestController Annotations are used to define web controllers as per the MVC Design pattern. A controller is responsible for handling HTTP requests and returning HTTP responses to the client.

The main difference between these two annotation is how they handle client’s request and when they are used

For example, @Controller Annotation is there from day 1, and it is used to mark a class as a web controller that processes HTTP requests and returns a view name, which is then resolved by a view resolver to generate the final HTML view.

On the other hand @RestController Annotation was added in later Spring versions, like Spring 3.4, to increase support for REST API development.

In the case of a REST API, instead of returning HTML, you will probably want to return JSON or XML, as your client is no longer human but a machine.

If you want to return JSON or XML from a Spring MVC controller, then you need to add @ReseponseBody Annotations for each of the Controller methods and it seems like overkill when implementing REST APIs using Spring and Spring Boot.

Designers and developers of the Spring Framework recognized this shortcoming and then added a new annotation called @RestController in the Spring 3.4 version.

The @RestController annotation is a combination of the @Controller and @ResponseBody annotations, and you can use it to implement REST APIs in Java and Spring Boot.

The key difference between @Controler and @RestController annotation is @ResponseBody annotation, @Controler does not automatically add the @ResponseBody annotation to all of the controller’s methods, which means that you need to add it to each method individually if you want to return a JSON or XML response.@RestController automatically adds the @ResponseBody annotation to all of the controller’s methods.

Now let’s see examples of both @Controller and @RestController annotations in the Spring framework

@Controller and @RestController Example in Spring Boot

Here is a simple example of @Controller annotation, which is used to return a response for simple HTTP requests on “/hello” path:

@Controller
public class MyController {

    @Autowired
    private MyService myService;

    @RequestMapping(”/hello”)
    public String sayHello(Model model) {
        model.addAttribute(”message”, myService.getHelloMessage());
        return “hello”;
    }
}

In this example, the MyController class is annotated with @Controller annotation and has a single method, sayHello(), which is mapped to the /hello URL.

The method takes a Model object as a parameter and adds an attribute called "message" to it. The method then returns a string “hello”, which is the name of the view to be rendered.

Now, let’s see an example of @RestController annotation from Spring Framework, which I used to respond to the REST API

@RestController
public class MyRestController {

    @Autowired
    private MyService myService;

    @RequestMapping(”/greeting”)
    public Greeting getGreeting() {
        return myService.getGreeting();
    }
}

In this example, the MyRestController class is annotated with @RestController and has a single method, getGreeting(), which is mapped to the /greeting URL.

The method returns an object of the Greeting class, which will be automatically converted to a JSON or XML representation and sent as a response to the client.

It is also worth noting that the Greeting class should have getters and setters For the data that you want to be included in the response, otherwise, it will not be serialized properly or converted to JSON.

Things to keep in mind while using @Controller and @RestController annotations in Spring Boot

Here are a few things to keep in mind while using the @Controller and @RestController annotations:

• When using the @Controller annotation, you will need to use the @ResponseBody annotation to return JSON or XML data, otherwise, the response will be treated as a view name, and a view resolver will try to resolve it. The @ResponseBody annotation is not necessary as it is already included in the annotation.

• When using the @RestController annotation, you can also use the @ResponseStatus annotation to set the HTTP status code of the response and the @RequestBody annotation to bind the request body to a method parameter.

• And while using the @RestController annotation, you can use the @RequestMapping annotation to set the URL path to which the methods apply.

• Another thing which is worth noting is that if you are returning a complex object that contains child objects, make sure all the child object classes have getters and setters, otherwise, they will not be serialized properly.

Java and Spring Interview Preparation Material

Before any Java and Spring Developer interview, I always used to read the below resources

Grokking the Java Interview: click here

I have personally bought these books to speed up my preparation.

You can get your sample copy here, check the content of it, and go for it

Grokking the Java Interview [Free Sample Copy]: click here

If you want to prepare for the Spring Boot interview you follow this consolidated eBook

Grokking the Spring Boot Interview

You can get your copy here — Grokking the Spring Boot Interview

That’s all about the difference between the @Controller and @RestController annotations in Spring Boot.

Just remember that @Controller is used to create web controllers that return views, which are further resolved by the view resolver, while @RestController Is used to create web services that return JSON or XML data. It’s also worth noting.

Hello folks, the title may sound like what?? Everyone is saying to use microservices, and you are saying otherwise? Why? Well, it’s not just me; many people think like that, and there are solid reasons for it, which you will see in this article.

This was also one of the questions that was asked of me last week for a big, reputable bank interview, so knowing when not to use Microservice architecture is as important as knowing when to break your monolith into Microservices.

By the way, if you are preparing for Java developer interviews, then I have shared many resources earlier, which you can also check out to prepare better.

By the way, if you are new to Microservice architecture or just want to revise key Microservice concepts and are looking for resources, then here are a few online courses you can join:

1. Master Microservices with Spring Boot and Spring Cloud [Udemy]

2. Building Scalable Java Microservices with Spring Boot [Coursera]

3. Developing Microservices with Spring Boot [Educative]

4. Master Microservices with Java, Spring, Docker, Kubernetes [Udemy]

This list includes both video and text-based courses as well as project-based courses for hands-on learning; you can join one or a couple of them to revise Microservices concepts.

10 Reasons Why You Shouldn’t Use Microservices for Everything

Microservices have become one of the most popular software architecture styles over the last decade. Companies like Netflix, Amazon, Uber, and Spotify have shown how powerful they can be when building large-scale systems.

But here’s the catch:

Microservices are not a silver bullet.

Many teams adopt them because they’re trendy, only to realize later that they’ve introduced unnecessary complexity, higher costs, and operational headaches.

Before splitting your application into dozens of services, consider these reasons why a monolith may actually be the better choice.

1. Increased Complexity

Nothing is free in software engineering.

When you move from a monolithic application to microservices, you’re no longer managing a single application—you are managing an entire distributed ecosystem.

Besides your business logic, you now need infrastructure for:

• Service Discovery (Eureka, Consul)

• API Gateway

• Load Balancer

• Configuration Management

• Distributed Logging

• Monitoring

• Circuit Breakers

• Retry mechanisms

• Message Brokers

• Service-to-service authentication

For example, imagine an e-commerce application.

In a monolith, placing an order might simply involve calling a Java method:

orderService.placeOrder();

In a microservice architecture, the same request may travel through:

Client
    ↓
API Gateway
    ↓
Order Service
    ↓
Inventory Service
    ↓
Payment Service
    ↓
Shipping Service
    ↓
Notification Service

Each network hop introduces another potential point of failure.

Instead of solving business problems, developers often spend time managing infrastructure.

2. Testing Becomes Much More Difficult

Testing a monolithic application is relatively straightforward.

You can start the application, connect to a database, and run integration tests.

With microservices, every service can have:

• Its own database
  

• Independent deployment
  

• Different APIs
  

• Different versions
  

Now testing becomes much harder.

You need to verify:

• Each service individually
  

• Communication between services
  

• API compatibility
  

• Failure scenarios
  

• Network timeouts
  

• Message queues
  

• Contract testing
  

For example, imagine the Payment Service changes its API.

Suddenly:

• Order Service breaks
  

• Billing Service breaks
  

• Refund Service breaks
  

Even though each service passes its own unit tests.

This is why many organizations invest heavily in:

• Contract Testing
  

• Consumer Driven Contracts
  

• End-to-End Tests
  

• Integration Test Environments
  

Testing quickly becomes one of the biggest engineering investments.

3. Higher Deployment Costs

A monolithic application usually means:

• One application
  

• One deployment
  

• One server (or a few replicas)
  

Microservices multiply everything.

Suppose your system has:

• User Service
  

• Order Service
  

• Payment Service
  

• Inventory Service
  

• Email Service
  

• Notification Service
  

• Recommendation Service
  

Now each service requires:

• Containers
  

• CI/CD pipeline
  

• Monitoring
  

• Logging
  

• Scaling
  

• Health checks
  

• Kubernetes deployment
  

• Cloud resources
  

Instead of deploying one application, you might deploy 40 or even 100 services.

Cloud costs increase quickly.

Even idle services consume CPU, memory, storage, and networking resources.

4. Increased Operational Overhead

Running one service is relatively easy.

Running 100 services is a completely different challenge.

Operations teams must monitor:

• CPU usage
  

• Memory usage
  

• Request latency
  

• Error rates
  

• Logs
  

• Certificates
  

• Configuration
  

• Secrets
  

• Autoscaling
  

• Database connections
  

If one service goes down, it can create a cascading failure across the system.

For example:

Inventory Service fails

↓

Order Service cannot reserve stock

↓

Checkout fails

↓

Payment requests fail

↓

Customers abandon carts

Even though only one service originally failed.

This is why companies need sophisticated monitoring tools like Prometheus, Grafana, Datadog, or New Relic.

Read more

Hello friends, in the last few years, Microservices have become the default architecture for building scalable, cloud-native applications. From startups to enterprises, teams are moving away from monoliths to distributed systems that are easier to scale, deploy, and evolve.

But microservices are also complex. Concepts like service boundaries, distributed transactions, observability, and event-driven communication can be difficult to master without strong fundamentals.

That’s where good books still shine. While courses and tutorials are helpful, books provide deeper explanations, real-world case studies, and architectural thinking that short-form content often misses.

In this article, I’ve curated 10 of the best microservices books that every developer, architect, and backend engineer should read to truly understand modern distributed systems.

1. Building Microservices: Designing Fine-Grained Systems — Sam Newman

This is widely considered the definitive introduction to microservices. Sam Newman explains not only how to build microservices, but also when not to.

The book covers:

• Service decomposition strategies

• Deployment and testing approaches

• Monitoring, logging, and scaling

• Trade-offs between microservices and monoliths

If you read only one book on microservices, start with this one.

Here is the link to get this book: Building Microservices: Designing Fine-Grained Systems

2. Microservices Patterns — Chris Richardson

Once you understand the basics, this book takes you deeper into production-grade patterns like:

• Saga pattern

• API Gateway

• Circuit Breaker

• Event sourcing and CQRS

It’s one of the most practical resources for solving real distributed system problems.

If you want an interactive companion, I found Grokking Microservices Design Patterns particularly useful.

3. Production-Ready Microservices — Susan Fowler

Many books explain architecture, but this one focuses on operating microservices in production — something that’s often overlooked.

You’ll learn:

• Service monitoring and alerting

• Failure handling and resiliency

• Deployment pipelines and scaling

• Incident response and operational maturity

If you’re moving toward a tech lead or platform engineering role, this book is essential.

Here is the link to get this book: Production-Ready Microservices

4. Monolith to Microservices — Sam Newman

Most real-world systems start as monoliths. This book provides a practical roadmap for migrating safely using:

• Strangler pattern

• Incremental refactoring

• Data migration strategies

• Versioning and backward compatibility

This is a must-read if you’re working on legacy modernization.

Here is the link to get this book — Monolith to Microservices

5. Building Event-Driven Microservices — Adam Bellemare

Modern microservices increasingly rely on event-driven architecture. This book explains:

• Event streaming and pub/sub

• Kafka vs RabbitMQ trade-offs

• Event sourcing and data consistency

• Designing scalable real-time systems

If your system needs asynchronous processing and high scalability, this book is invaluable.

Here is the link to get this book — Building Event-Driven Microservices

6. Microservices in Action — Morgan Bruce & Paulo Pereira

This is a very hands-on book focused on building microservices with:

• Spring Boot

• Docker

• Kubernetes

• REST and messaging systems

For Java developers, this is one of the best practical implementation guides.

You can also pair it with Design Microservices Architecture with Patterns & Principles for hands-on learning.

7. Domain-Driven Design — Eric Evans

Even though this book isn’t specifically about microservices, it introduced concepts like:

• Bounded contexts

• Aggregates

• Ubiquitous language

These ideas are fundamental to designing well-structured microservices. It’s dense, but incredibly influential.

Here is the link to get this book — Domain-Driven Design

8. Implementing Domain-Driven Design — Vaughn Vernon

If Eric Evans’ book explains the theory, this one shows how to apply DDD in real projects and microservice architectures.

It’s especially useful for architects designing service boundaries and domain models.

Here is the link to get this book — Implementing Domain-Driven Design

9. Architecture Patterns with Python — Harry Percival & Bob Gregory

For Python developers, this book is a gem. It combines:

• Domain-driven design

• Test-driven development

• Event-driven microservices

It’s one of the few resources that show how to implement clean architecture patterns in Python microservices.

Here is the link to get this book — Architecture Patterns with Python

10. Microservices: Up and Running — Ronnie Mitra & Irakli Nadareishvili

If you’re completely new to microservices and want a step-by-step introduction, this is one of the most beginner-friendly books available.

It explains:

• Core architecture concepts

• Communication patterns

• Deployment and security basics

You can combine it with the Master Microservices with SpringBoot, Docker, and Kubernetes for hands-on practice.

Which Microservices Book Should You Read First?

If you’re unsure where to start, here’s a simple roadmap:

• Beginner: Microservices: Up and Running

• Architecture Fundamentals: Building Microservices

• Design Patterns: Microservices Patterns

• Event-Driven Systems: Building Event-Driven Microservices

• Legacy Migration: Monolith to Microservices

Final Thoughts

That’s all on the best books you can read to learn Microservices architecture in 2026. Microservices are powerful, but they introduce challenges around data consistency, network latency, observability, and operational complexity.

The books above provide the depth and practical insights needed to navigate these challenges confidently.

If you’re serious about becoming a strong backend engineer or software architect, these books will help you build the mental models required to design reliable, scalable distributed systems.

If you enjoyed this list, you may also like:

• Grokking Microservices Design Patterns

I receive lots of emails and Facebook chats related to Java, but one of the most common questions which I have mostly asked is, which is the best book to learn Java? I have just started learning Java, Could you please recommend a good core Java book to me?

After answering in one word too many of those readers and new Java developers as “Head First Java,” I thought to write this blog post, Why? because some of them came back to me and asked me, “Can you please recommend a serious Java book, not the comics?”

Well, I didn’t like that comment, but then I thought it’s natural that not everyone will find “Head First Java” as useful as I have always found.

Many programmers don’t like jokes, diagrams, and light way to learn, which is what the Head First series provide. It’s also natural that some people will connect to some authors better than others, and many people find the Head First series wordier, much like Java.

So, I thought to spend some time and find out a couple of options for my newbie Java developers. Thankfully, there are some excellent Java books available in the market, which is up-to-date, comprehensive, yet easy to understand, and this article is a collection of those books.

Btw, if you prefer online courses, then The Complete Java Masterclass on Udemy is also an excellent place to start.

How to Learn Java from Scratch in 2026?

If you are new to programming as well as new to Java, then here are my 5 tips to learn Java quickly:

1. Read a book daily

Whichever book you choose from this list, make sure you read it daily until you learn the concepts. It may take a couple of weeks to a couple of months, but you should make a habit of reading the book daily. This will ensure that you not only read but also understand concepts.

2. Write code daily

Reading a book is essential, but it is a passive action, and that’s why you must write code daily, as given in the book. Start with just typing the program provided in the book, it will develop some code writing habit and code reading ability. Slowly, you will develop the code sense, which tells you what the code is doing and how.

3. Do all exercises

You must do all the exercises given in the book. All beginner’s books have the right amount of practice to exercise your mind that it must recall the concept it has learned. This is also one of the main criteria for choosing a book for beginners.

By doing Java exercises, you will not only rinse and repeat concepts but also actively participate in reading, writing, and thinking. This muscle exercise is essential to retain the ideas you have learned.

4. Keep referring book whenever you get stuck

When you do exercise, don’t hesitate to look back when you are stuck. In fact, looking back to refer to the relevant concept is one of the objectives of exercises. Practices make perfect, and repetition is a part of the practice.

5. Talk to Someone

Find a buddy who is also learning Java, talk to him, discuss him, tell him about the new concept you have just learn. When we try to explain something to anyone, we learn it better.

Also, the exchange of knowledge fills gaps and create a path for more in-depth learning. And if you really love Java, prepare for Oracle Java Certification, there is no better way to learn Java in quick time then preparing for SCJP or OCPJP.

4 Books to Learn Java from Scratch in 2025

Here are my recommended four Java books for a beginner who wants to learn Java in a couple of months. You can learn Java in a couple of months, provided you read the book daily, practice code, and discuss whatever you have learned to your buddy.

1. Head First Java 3rd edition

My first recommendation is still Head First Java because it is the best book to learn Java. If you can connect to head the first series then you will love it, you will read it on your commute, read it before sleeping and read it on your iPad, Yes it is that good.

It explains critical concepts of Object-oriented programming, and Java is simple words with pictures, diagrams, and dialog. There different kinds of exercises, puzzles, and fill in the blanks also helps to revise the concept regularly, and the best thing is their section of “No question is a dumb question,” you will love it.

For the best result, you should combine this book with a new and up-to-date course like The Complete Java Masterclass by Tim Buchalaka on Udemy. This means you will not only have best start but also upgrade your knowledge at the same time.

The Head First Java provides knowledge in such a way that you will feel understanding Java in no time. The coding part is a little light, but if you follow code sample and use the book along with an IDE, then you will learn more quickly.

2. Java: A Beginner’s Guide 13th Edition (Covers Java 21)

The Java: A Beginner’s Guide, 13th Edition, is another excellent book for beginners. This is the book I used to learn Java long back and now I am glad to see the 13th Edition of this book, which shows how popular it was.

I mean, to be honest, how many books have you seen which have got 13th edition published? Not many right? But, this book is one of those classic book which most of Java developer have used.

It is straightforward and - importantly - has been kept up-to-date, I mean it covers Java S.E. 11 as well. The Kindle version is also available.

Many online courses in Java uses this book as their study guide too, for example, you can also join Java In-Depth: Become a Complete Java Engineer! to learn along with this book.

3. Core Java by Cay Horstmann

I didn’t know about Cay Horstmann until I read his book “Java S.E. 8 for Impatient,” and I just loved his writing skill. He is really great in explaining things, so when I started my search about a beginner’s Java book, my first search goes to Cay Horstmann’s books and looks at what, I found a gem in the name of “Core Java” series of books.

This is not like Head First Java, it’s like a regular book with lots of information, code sample, and explanation. Programmers who don’t like Head First Java for his lightness will definitely appreciate Cay Horstmann’s comprehensive coverage in simple words.

There are two parts of Core Java series, Core Java Volume 1, which covers fundamentals and Core Java Volume 2 for the advanced feature.

The only drawback of this book is that you need some programming experience; it’s not for a complete newbie who doesn’t know anything about programming. It’s best suited for experienced programmers.

This is what the author says about the book “The book is aimed at experienced programmers who want to learn how to write useful Java applications and applets. No hype, no toy code, no language layering, just solid facts, and in-depth research to help you write real programs”.

If you want, you can also combine this book with The Java Programming for Complete Beginners course by Ranka Karnam, a fellow Java blogger and Udemy best selling instructor. He will tell you how you can use JShell to speed up your learning in Java.

4. Thinking in Java

This is my third recommendation to anyone who wants to learn Java. I had read this book when I started learning Java, so I can safely say that No other book is better than “Thinking In Java” for programmers who are starting to learn Java. Bruce Eckel’s books are a pleasure to read, and the examples are easy to follow.

The most recent version of this book is “Thinking in Java 4th Edition” and covers most of the Java features, except what is introduced in Java 7 and 8. You will learn advanced features like Enum, Generics, Annotation, etc.

Like the previous two books, this also got lots of exercises to keep you busy and give you enough hands-on practice in Java.

If you want, you can also combine this book with the Java Fundamentals Part 1 and 2 courses on Pluralsight, which is also an excellent companion for beginners.

5. Learn Java the Easy Way: A Hands-On Introduction to Programming

This is another great book to learn Java from scratch. This book is perfect for beginners who are new to both Java and programming and looking for a book which can not only teach them Java rules and syntax but also programming in general.

The good thing about this book is that it will teach you Java using JShell, Java’s interactive command line shell that allows programmers to run single lines of code and get immediate feedback.

You will then create a guessing game, a secret message encoder, and a multitouch bubble-drawing app for both desktop and mobile devices using Eclipse, an industry-standard IDE, and Android Studio, the development environment for making Android apps.

While building app you will learn many key things like

• How to do calculations, manipulate text strings, and generate random colors in Java

• How to use conditions, loops, and methods to make your programs responsive and concise

• How to create functions to reuse code and save time

• How to build graphical user interface (GUI) elements, including buttons, menus, pop-ups, and sliders

• How to take advantage of Eclipse and Android Studio features to debug your code and find, fix, and prevent common mistakes

Overall a great hands-on book to learn Java from scratch. I highly recommend this to all the people who wants to start with Java and Programming. If you want more hands-on practice, you can also combine this book with the Java Bootcamp: Learn Java with 100+ Java Projects course on Udemy where you will learn Java by building big and small 100+ projects.

Though there are many more Java books are there but to start with I will only recommend these five books. Try these four and pick the one which best suited you. Once you spend a couple of months in Java and can write a Java program in IDE fluently, you can check out my list of advanced Java books to expand your knowledge in other key areas.

For example, as a Java developer, you need to master Java Collections, Concurrency, JUnit testing, Java Performance Optimization, and Design patterns. There are so many things to learn in Java, but better to start with one book and follow it properly.

As I said before, it’s easy to learn Java, but it’s very difficult to master Java programming language because it is vast, there are so many things to learn in terms of language, API, frameworks, and libraries.

So, if you want not just to learn Java but to master, I suggest you look at following recommendations to make yourself strong on important areas like concurrency, collections, spring, hibernate, unit testing, JVM internals, and performance tuning.


Thanks for reading this article. If you like my Java books recommendations, then please share it with your friends and colleagues. If you have any feedback, suggestion, or any good book you think a programmer should read to learn Java, then please share with us via comment.

Btw, if you like to learn from tutorial then book, then just start with Oracle’s official Java tutorial here (http://docs.oracle.com/javase/tutorial/)

P. S. - If you prefer online courses, then The Complete Java Masterclass on Udemy is also an excellent place to start with.

Hello guys, if you are looking for some interesting novel-style tech books to read this weekend, then you have come to the right place.

Earlier, I shared the best software architecture books, and in this article, I am going to share the best tech books for experienced developers to not just learn technical skills but also operational and management skills in a fun and easy way.

I always like to read books that are interesting first and informative later because it’s very difficult to go through a book that is informative but not interesting, and that’s why when I found an interesting tech book that is full of life lessons, I buy it and read it again and again, and this book list is a collection of such books.

Someone told me long ago that if we learn from our mistakes, then we can only learn a few things, but if we learn from others’ mistakes, then there is no limit, and what better way to learn from someone’s mistake than reading books.

I have been an avid reader throughout my life. When I was a kid, I used to read Comics and Kids’ stories. While I was growing up, I started reading magazines and novels, and when I started with Programming and Software development, I started exploring technical books.

Even after so many years of tech and programming and a busy schedule, I make an effort to read books, particularly while traveling.

Last year, I also read a few interesting tech books, which I really liked and thought to share with you guys. These are the books that are highly recommended for senior developers, project managers, tech leads, team leads, solution architects, and programmers.

I won’t say that you will find every book as good as I did, but they are worth checking. If you connect and like, go on to read the book and learn something from it to make your life better.

This is also for many of my readers who have asked repeatedly about interesting tech books that are not about programming languages, frameworks, or tools, but about mindset, skills, and daily programmer life.

7 Best Tech Books for Experienced Programmers, Leads in 2026

Without wasting any more of your time, here is my list of the best technical, programming, software development, delivery, and DevOps books for experienced developers.

They will enhance your knowledge of software development and process, and they are also very easy to read; many of them are written in a novel style, so you will not only learn but also enjoy reading them.

1. The Phoenix Project

This is one of the great books that has been there for quite some time, but I only found it last year, yes, it happens. The last time it happened to me was when I discovered Clean Code after spending a few years in programming.

This is a must-read for any senior developer and tech lead as it summarizes how automation and DevOps help and why every developer should have that mindset.

It’s like a novel and very interesting to read. You won’t feel bored. I actually finished the whole book on a long train journey.

If you can read just one book this year, I would recommend this book. It will change your mindset and shape you into a better programmer, developer, and person.

2. The Unicorn Project

This is the follow-up book to The Phoenix Project. The full title of the book is “The Unicorn Project - A Novel about Developers, Digital Disruption, and Thriving in the Age of Data”.

If you haven’t, this is again a tech book presented as a novel, which means getting through it won’t be a hard effort.

The book follows the life of one of the senior lead developers and architects from The Phoenix Project. Gene Kim is a great storyteller, and if you liked The Phoenix Project, you will also love this book.

It’s not only interesting to read but at the same time very enlightening and provides some useful insights to improve your technology business.

3. Site Reliability Engineering

The Site Reliability Engineering: Google’s Secret Sauce For High Availability And Happy Ops is another great book for experienced programmers, support people, operations, and tech leads in general. I’ve read this book recently, and for me, it was a fantastic experience.

I learned how things are really done at scale, or should I say at Google scale, and the fact that no one is perfect, but with proper organization, skills, and training, they can handle anything.

The Kindle version of this book, which is also known as Site Reliability Engineering: How Google Runs Production Systems, is also available at just $27.99

4. Accelerate: Building and Scaling High-Performing Technology Organizations

This is another awesome course for senior developers and tech leads who want to build high-performance solutions for their organization. Accelerate Building and Scaling High-Performing Technology Organizations by Nicole Forsgren, Jez Humble, and Gene Kim, author of the first book in this list, The Phoenix Project.

Through four years of groundbreaking research, Dr. Nicole Forsgren, Jez Humble, and Gene Kim set out to find a way to measure software delivery performance--and what drives it--using rigorous statistical methods.

This book presents both the findings and the science behind that research. You will discover how to measure the performance of their teams and what capabilities they should invest in to drive higher performance.

In short, one of the best books for technical leads and CIOs who want to build and scale a high-performance technology organization.

5. Continuous Delivery

If you are looking to set up CI and CD in your project or just starting with DevOps, then this is a must-read book for you. Getting software released to users is often a painful, risky, and time-consuming process.

This book sets out the principles and technical practices that enable rapid, incremental delivery of high-quality, valuable new functionality to users.

Through automation of the build, deployment, and testing process, and improved collaboration between developers, testers, and operations, delivery teams can get changes released in a matter of hours, sometimes even minutes-- no matter what the size of a project or the complexity of its codebase.

Jez Humble and David Farley, authors of this course, start by presenting the foundations of a rapid, reliable, low-risk delivery process.

After that, they introduce the “deployment pipeline,” an automated process for managing all changes, from check-in to release. Finally, they discuss the “ecosystem” needed to support continuous delivery, from infrastructure, data, and configuration management to governance.

In short, one of the best books to learn about continuous Developers and a must-read book for senior developers, tech leads, and DevOps champions.

6. Building Microservices: Designing Fine-Grained Systems 2nd Edition

With the growth of Cloud Computing, the adoption of Microservice architecture is also growing because it goes hand-in-hand with cloud computing.

Both deployment and scalability of Microservices are easier compared to a Monolithic application, and that’s why many companies, both big and small, are using Microservices.

But developing Microservices brings its own set of challenges and problems, and that’s where this book helps you.

With lots of examples and practical advice, this book takes a holistic view of the topics that system architects and administrators must consider when building, managing, and evolving Microservice architectures.

This is one of the best books to learn about Microservices, and I highly recommend this book to senior Java developers, programmers, Tech leads, and solution architects who are responsible for making tech-related decisions.

7. Team Topologies

This book is a bit different than the previous books on this list as it doesn’t focus on technologies, but it teaches you something more important: managing teams.

This is a fantastic book to read if you manage a software development team. Even if you are not managing teams, you will learn a lot about improving teamwork by going through the case studies and the synthetic approach presented in this book.

It provides a lot of deep insights for organizing the development team for high performance. I highly recommend this book to every team lead and project manager to read, especially if you are managing a remote development or support team at this time.

That’s all about some of the best books senior developers, tech leads, and software programmers can read in 2026. There is a good chance that you might have read many of these books already, which is great. You may be able to suggest a few more books to my readers and me.

If you want to do just one thing at this moment, I would suggest just going and reading “The Phoenix Project”.

Hello guys, Artificial Intelligence is one of the growing fields in technology, and many developers are trying to learn Artificial Intelligence to take their career next level.

If you are a Python developer and looking for the best courses to learn Artificial Intelligence with Python, then you have come to the right place. In this article, I will share the best courses to learn AI basics as well as some hands-on courses to practice AI with the Python library.

I first came across AI when DeepMind beat Garry Kasparov, one of the finest players of Chess. It was way back in the 1990s, and AI has come a long way since then.

Now, Google is using the same DeepMind to reduce the electric bills of its Data Center by 40%, and Elon Musk is talking about self-driving cars.

If you are a tech geek or a programmer, these are some of the fascinating examples of Artificial Intelligence, and every time I hear an AI story, I really get hooked. While all these are good and interesting, as a programmer, I am also spending some time on how to code AI.

I am not talking about big problems, but just learning how to use AI for your benefits or customize Artificial Intelligence for your own needs will go a long way in the near future, and I am learning all these by joining the online courses and looking at agents on sites like OpenAI Gym.

Since Games are my other interest area, if you don’t know, I learned to code by writing games like Tic-Tac-Toe, Breakout, Tetris, and Chess, and developing AI to build games seems a great idea to learn Artificial Intelligence.

If you look closely, games provide a perfect environment for building Artificial Intelligence. When you have an algorithm that can beat the game, you know that the same principles can be applied to solve real-world problems. This is precisely the approach many tech giants like Google and Tesla follow.

If you are like me and want to learn AI or Artificial Intelligence in 2026 with Python and are looking for some excellent online courses, then you have come to the right place. In this article, I am going to share some of the best online courses to learn Artificial Intelligence with Python in 2026.

10 Best Udemy and Coursera Courses to Learn Artificial Intelligence in 2026

There are many courses to learn Artificial Intelligence on the Internet, but most of them are boring or too technical, even for programmers with years of experience.

Since I always believe in simplicity and exciting stuff, I have only chosen a course that has the right mix of theory and practice. These are the courses that not only teach you how to build Artificial Intelligence but also inspire you to learn AI.

1. AI for Everyone by Andrew Ng (Coursera)

Andrew Ng is one person who has inspired millions of developers about Artificial Intelligence and Machine learning through his classic machine learning courses. He is also the founder of Coursera, the lead of Google Brain, Chief Scientist of Baidu, and an instructor of the most popular Machine Learning course on the planet.

If you are wondering if this is true? Yes, the Machine Learning course on Coursera has been taken by more than 4.6 million students, which makes it a popular course on Machine Learning. You can check the numbers by yourself.

When Andrew Ng announced this course on Twitter, I knew this was something I must join. Even though I had attended a couple of classes on Artificial Intelligence, I learned a lot from this course.

Here is the link to join this course — AI for Everyone by Andrew Ng

This course provides a comprehensive overview of what AI is and the meanings of various concepts being talked about in the context of Artificial Intelligence. It helps you to build your vocabulary so that you can discuss AI with fellow programmers and other people, both online and offline.

By the way, if you find Coursera courses useful, then I also suggest you join Coursera Plus, a subscription plan from Coursera that gives you unlimited access to their most popular courses, specializations, professional certificates, and guided projects. It costs around $399/year, but its complete worth it as you get unlimited certificates. Now its also 40% OFF.

2. Artificial Intelligence A-Z™: Learn How To Build An AI (Udemy)

This was the first course I took for learning Artificial Intelligence, and it was no-brainer because I am a massive fan of instructor Kirill Eremenko and his SuperDataScience team.

Having attended his in-depth learning course, I know how engaging it is, which is my primary requirement, given the complexity of the subject.

I didn’t want to get bogged down by heavy use of Mathematics and neural networks, instead, I wanted a course that can inspire me to learn more, and I must say, I wasn’t disappointed.

This course will teach you how to combine the power of Data Science, Machine Learning, and Deep Learning to create powerful AI for Real-World applications like creating AI to beat the game in Breakout, pass a level in Doom and create logic for self-driving cars.

Talking about social proof, more than 101,411 students have already enrolled in this course, and it has, on average, 4.4 ratings from 11,452 ratings, which is just phenomenal.

A big thanks to Kirill Eremenko and his entire SuperDataScience team for creating this awesome course.

Here is the link to join this course — Artificial Intelligence A-Z™: Learn How To Build An AI (Udemy)

3. The Beginner’s Guide to Artificial Intelligence in Unity. (Udemy)

This is another course I have taken on Udemy to learn about how to use AI in games. If you don’t know, Unity is one of the most popular game engines for developing all kinds of video games, and they are using AI to make games difficult depending upon the player’s caliber.

If you are a game designer or game developer and if your non-player characters lack drive and ambition, then you can join this course to learn how to make them more authentic and believable.

In this course, Penny, the instructor of the course, reveals the most popular AI techniques used for creating believable character behavior in games using her internationally acclaimed teaching style and knowledge from over 25 years working with games and graphics, and has written two award-winning books on game AI.

Throughout this course, you will follow along with hands-on workshops designed to teach you about the fundamental AI techniques used in today’s games. If you love video games and want to learn more about how AI is used there, this is the course for you.

Here is the link to join this course — Beginner’s Guide to Artificial Intelligence in Unity

4. Introduction to Artificial Intelligence (AI) Coursera

This is another fantastic course to learn about Artificial Intelligence in Coursera. In this course, you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts, and terms like machine learning, deep learning, and neural networks.

You will also be exposed to various issues and concerns surrounding AI, such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI.

You will also demonstrate AI in action with a mini-project, and get a certificate after completing the project successfully

This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone, whether you have a technical background or not.

Here is the link to join this course — Introduction to Artificial Intelligence (AI)

5. Artificial Intelligence: Reinforcement Learning in Python Udemy

If you don’t know, Reinforcement Learning is a big part of AI, and this course provides a complete guide to Deep Reinforcement Learning.

It helps you to understand reinforcement learning on a technical level. It also helps you to understand the relationship between reinforcement learning and psychology.

Reinforcement learning has recently become famous for doing some of the fantastic things in AI.

Much like deep learning, a lot of the theory was discovered in the 70s and 80s, but it hasn’t been until recently that we’ve been able to observe firsthand the amazing results that are possible.

Talking about social proof, this course has been trusted by more than 58,000 students, and it has, on average, 4.6 ratings from close to 15,000 participants, which is just phenomenal. If you are looking for a purely technical course in AI, this is the one to join.

Here is the link to join this course — Artificial Intelligence: Reinforcement Learning in Python

6. IBM AI Engineering Professional Certificate — Coursera

This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.

In this best Coursera AI course, you’ll learn fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python.

You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.

Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.

In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in AI engineering.

Here is the link to join this course — IBM AI Engineering Professional Certificate

7. Generative AI Fundamentals Specialization by IBM

In this Coursera course, you will learn how to quickly and easily get started with Generative Artificial Intelligence.

You will understand how Gen AI works, become familiar with its use cases and real-life client examples, and be introduced to several of Gen AI services from IBM that enable anyone to easily apply AI and build smart apps. You will also work with several Watson services to demonstrate AI in action.

This course is also part of the following specialization and certificates:

• AI Foundations for Everyone Specialization

• IBM Applied AI Professional Certificate

Which means completing this course will count towards your learning in any of the above programs

This course does not require any programming or computer science expertise and is designed for anyone, whether you have a technical background or not.

Here is the link to join this course — Generative AI Fundamentals Specialization by IBM

8. Tensorflow 2.0: Deep Learning and Artificial Intelligence

This is for serious people who want to learn AI by building models and solving real-world problems. In this course, you will learn Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!

This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it.

Here is the link to join this course — Artificial Intelligence for Business

9. Artificial Intelligence for Business

This is another great Udemy course to learn Artificial Intelligence for business people. Created by Kirill Eremenko and his SuperDataScience team, this course will teach you how to solve Real-World Business Problems with AI Solutions

You will go through a couple of case studies to learn how to Optimize Business Processes, Minimize Costs, and Maximize Revenue. With this course, you will get a 100-page book covering everything about Artificial Intelligence for Business!.

Here is the link to join this course — Artificial Intelligence for Business

10. Artificial Intelligence Masterclass — Udemy

This is the ultimate course to learn AI on Udemy. In this course, you will get 10 hours step by step guide and the full roadmap to build your own Hybrid AI Model from scratch.

You will also learn how to develop the most powerful Artificial intelligence model based on the most robust Hybrid Intelligent System. So far, this model proves to be the best state-of-the-art AI ever created, beating its predecessors at all the AI competitions with incredibly high scores.

In addition to all this, we will also get the full implementations in the two AI frameworks: TensorFlow and Keras.

Here is the link to join this course — Artificial Intelligence Masterclass

That’s all about some of the best Udemy courses and Coursera certifications to learn Artificial Intelligence or AI with Python in 2026. I have tried to include some non-technical courses on Artificial Intelligence, like AI for Every One by the great Andrew Ng, just to understand the business and general aspect of AI, which is far more important than actually learning how to build AI for a particular domain or problem.

Thanks for reading this article so far. If you like the best Artificial Intelligence or AI courses from Udemy and Coursera, then please share them with your friends and colleagues. If you have any questions or feedback, please drop a note.

P. S. — If you are serious about moving into the AI and Machine Learning field, I also suggest you learn Python, one of the most valuable skills when it comes to Machine Learning and AI. If you need resources, check out this Complete Python Developer in 2026: Zero to Mastery to kick-start your journey.

Hello guys, Agentic AI is rapidly becoming one of the most exciting frontiers in artificial intelligence. Unlike traditional AI systems, Agentic AI focuses on building autonomous agents — intelligent programs that can reason, plan, and act independently to achieve specific goals.

From AI assistants that manage complex workflows to autonomous trading systems, agentic architectures are now at the heart of cutting-edge applications.

As AI agents move from research labs to real-world use cases, the demand for engineers who can design and deploy these systems is exploding.

Whether you want to create multi-agent workflows, automate business processes, or simply stay ahead in the AI job market, learning Agentic AI in 2026 is a smart career move.

If you want to learn and master Agentic AI and are looking for resources, then you have come to the right place. This post contains the 10 best resources — courses, books, and interactive guides — that will teach you how to build intelligent AI agents from the ground up.

These resources come from trusted platforms like Udemy, Coursera, Educative, Datacamp, and Frontend Masters, as well as popular books like The Agentic AI Bible by Thomas R. Caldwell

10 Best Resources to learn Agentic AI in 2026

Without any further ado, here are the 10 best resources you can use to learn and master Agentic AI in 2026 and learn to build production-quality AI agents in no time. These resources include both books and online courses from sites like Udemy, Coursera, DataCamp, Frontend Masters, Udacity, and ZTM Academy.

1. The Complete Agentic AI Engineering Course (2026) — Udemy

If you want one comprehensive, project-based course to master Agentic AI, this is it. The course takes you from the basics of AI agents to building 8 real-world projects using tools like OpenAI Agents SDK, CrewAI, LangGraph, AutoGen, and MCP.

Key Highlights:

• Career Digital Twin: Deploy an agent that represents you to potential employers.

• SDR Agent: Build a sales automation agent that sends professional emails.

• Deep Research: Create a multi-agent research team for any topic.

• Stock Picker Agent: Automate investment analysis with CrewAI.

• Capstone Trading Floor: Deploy 4 autonomous trading agents powered by MCP servers.

[Bestseller | 4.7 ⭐ (14,746 ratings) | 114,822 students]

Why Take This Course:\ It’s one of the most practical and up-to-date Agentic AI resources available. Perfect for engineers who learn best by building.

Here is the link to join this course— The Complete Agentic AI Engineering Course

2. Agentic AI Nanodegree — Udacity

Why it’s the most comprehensive option: If you want to go all-in on Agentic AI, the Udacity Nanodegree is the most rigorous, career-focused program available. At 53 hours and rated 4.8/5 by 51 reviewers, it’s not just a course — it’s a complete specialization.

This intermediate program takes you from advanced prompting (Chain-of-Thought, ReAct) through sophisticated multi-agent orchestration.

You’ll build a portfolio of real projects: a multi-agent travel planner, an AI-powered project manager, and a fully automated sales system. These are portfolio pieces that actually impress employers.

Time commitment: 53 hours (roughly 6–8 weeks at 8–10 hours/week)

Career value: A Nanodegree credential is recognized by employers. This is more valuable than a single course certificate.

Join the program: Agentic AI Nanodegree

3. IBM RAG and Agentic AI Professional Certificate — Coursera

This isn’t a basic course. It covers Retrieval-Augmented Generation (RAG) pipelines, enterprise AI architecture, and how to build production-ready Agentic systems at scale. You’ll understand how Fortune 500 companies approach Agentic AI deployment.

What sets it apart:

• IBM Skills Network backing (enterprise credibility)

• Focus on RAG architectures for knowledge-intensive agents

• Enterprise-scale deployment patterns

• IBM Watson integrations

• Recognized professional certificate

Certificate value: Adds credibility to your LinkedIn profile and resume. IBM credentials carry weight.

Here is the link to join this AI certificate — IBM RAG and Agentic AI Professional Certificate

Pro tip: If you plan to take multiple Coursera courses, consider Coursera Plus (~$399/year) for unlimited access to 10,000+ courses and certificates. They are offering 50% discount now, so you can get it for just $199.

4. Build AI Agents and Multi-Agent Systems with CrewAI — Educative

CrewAI is one of the hottest frameworks for designing goal-driven agents, and this Educative course is the best way to learn it.

Here is why you should join this course:

• Design structured agent roles and collaborative teams.

• Integrate LLMs into real-world tasks.

• Automate workflows with Python.

Rating: 4.5 ⭐ | Level: Intermediate | Duration: 2h 15min

Here is the link to join this course — Build AI Agents and Multi-Agent Systems with CrewAI

5. Agentic System Design— Educative

If you want to learn how to architect large-scale agentic systems, this course delivers a deep dive into system design for multi-agent environments.

Here are the key features of this interactive Agentic AI course:

• System-level thinking for complex agents.

• Advanced tools: RAG, vector databases, LangChain.

• Real-world case studies for applied learning.

Rating: 4.7 ⭐ | Level: Advanced | Duration: 6h

Here is the link to join this course — Agentic System Design

By the way, you need an Educative Unlimited subscription to access these courses, and the best thing is that you can get it now for 50% discount as Educative is offering 55% discount on annual plans on Educative Premium Plus. I highly recommend you get this plan while the sale is on.

6. Agentic AI and AI Agents: A Primer for Leaders— Vanderbilt University (Coursera)

If you’re a manager, product leader, or founder looking to understand the strategic impact of Agentic AI, this Coursera course is perfect.

• Taught by Dr. Jules White, a top instructor in AI systems.

• Covers the fundamentals of AI agents and how they fit into business workflows.

• Over 57,000 enrolled learners.

Here is the link to join this course — Agentic AI and AI Agents: A Primer for Leaders, Vanderbilt University

7. AI Agents and Agentic AI in Python: Powered by Generative AI — Coursera Specialization

Another excellent Coursera track for developers who want a hands-on introduction to AI agents in Python.

Here are key skills you will learn in this course — -

• Build resilient AI agents using GenAI frameworks.

• Develop end-to-end agentic workflows.

• Future-proof your Agentic AI skills.

Here is the link to check this specialization on Coursera — AI Agents and Agentic AI in Python

By the way, if you are planning to join multiple Coursera courses or specializations, then consider taking a Coursera Plus subscription, which provides you with unlimited access to their most popular courses, specializations, professional certificates, and guided projects.

It costs around $399 per year but is worth it because you get access to more than 10000+ courses and projects, and you can also get unlimited certificates.

8. The Agentic AI Bible — Book

Written by Thomas R. Caldwell, author of The AI Engineering Bible, this book is the most comprehensive written reference on Agentic AI.

Why Read:

• Covers both theory and practice.

• Explains agent orchestration, tools, and real-world patterns.

• Great for engineers and AI strategists alike.

It’s also got great reviews and ratings, which show a lot about the usefulness of this book.

Here is the link to get this book — The Agentic AI Bible

9. Building Agentic AI Systems — — Book

If you prefer a technical, hands-on book, this is an excellent choice.\ It walks you through:

• Building autonomous agents step by step.

• Designing multi-agent ecosystems.

• Real-world deployment patterns.

I had also recommended this book in the past when I wrote about 5 must-read AI engineering books, and I am recommending it here again because it’s a great resource for anyone who wants to build AI agents in 2026.

Here is the link to get this book — Building Agentic AI Systems

10. Designing Agentic Systems with LangChain — DataCamp

This interactive short course is ideal for intermediate learners who want to design custom LangChain agents from scratch.

Key Skills:

• Build LangChain-powered agents.

• Create structured workflows for advanced use cases.

• Understand key tools and vector databases.

Here is the link to join this course — Designing Agentic Systems with LangChain

By the way, you need a Datacamp plan to access this course. They have different plans, like standard, professional, and premium, which allow access to all projects. I generally recommend the standard plan because it is right-priced and you get access to all the essentials to grow your data skills.

11. Complete Agentic AI Bootcamp with LangGraph and LangChain — Udemy

If you specifically want to master LangGraph and LangChain, two of the most popular frameworks for multi-agent systems, this bootcamp is a perfect fit.

In this course, you will learn to build autonomous apps with real-world workflows, including task orchestration, knowledge retrieval, and goal-driven execution.

Along the way, you will learn crucial skills like building AI agents using LangGraph, including creating workflows, managing agent state, memory, and event-driven behavior.

You will also develop and deploy multi-agent collaborative systems that can communicate, reason, and solve complex problems together.

Here is the link to join this course — Complete Agentic AI Bootcamp with LangGraph and LangChain

12. Learn Agentic AI — Build Multi-Agent Automation Workflows — Udemy

This course is ideal if you want to focus on multi-agent automation workflows. Using AutoGen and MCP, you’ll build six agents capable of handling real-world automation tasks.

Here are the key skills you will learn in this course:

• Build collaborative AI teams.

• Create self-improving agentic workflows.

• Deploy agents for end-to-end automation.

Talking about social proof, more than a thousand students have already joined this course.

Here is the link to join this course — Learn Agentic AI — Build Multi-Agent Automation Workflows

Bonus Resources

Apart from this, there are a few more resources you can explore on bootcamp-style sites like Frontend Masters and ZTM Academy:

1. Build an AI Agent from Scratch — — Frontend Masters

2. Build AI Agents with CrewAI on ZTM Academy — If you are looking for a bootcamp-style course to learn how to build AI Agents, then this course is for you.

Final Thoughts

That’s all about the best resources to learn Agentic AI in 2026. The era of Agentic AI is just beginning, and the opportunities are limitless. Whether you want to launch AI products, automate workflows, or secure a top AI engineering job, these 10 resources give you everything you need to become an Agentic AI expert in 2026.

If you prefer a single course that combines everything — projects, tools, and cutting-edge frameworks start with the Complete Agentic AI Engineering Course (2026). It’s the fastest way to gain job-ready skills while building real-world projects. It’s also very affordable.

The future belongs to those who can design intelligent, autonomous systems, and 2026 is the perfect year to start mastering Agentic AI.

Thanks a lot for reading this article so far. If you like these Agentic AI resources, then please share them with your friends and colleagues. If you have any feedback or questions, please drop a note.

P. S. — If you prefer reading books, watching courses, and doing projects, then you should start with The Agentic AI Bible by Thomas R. Caldwell, author of another popular book, The AI Engineering Bible, great for general AI introduction; both of his books are gems for AI Engineering.

Image credit - Paul Iustzin, author of LLM Engineering Handbook

Hello guys, Agentic AI, systems of autonomous agents that plan, act, and coordinate, is shaping up to be one of the most important trends in AI development.

Frameworks that enable multi-agent orchestration, tool integration, memory, reasoning, and collaboration are now becoming critical skills for engineers and developers.

Here are 10 frameworks you should be familiar with in 2026 — and for each, I’ve added a recommended Udemy course to get you up to speed.

1. AutoGen (Microsoft)

An open-source, multi-agent framework from Microsoft designed for scalable agent systems, inter-agent communication, and orchestration.

Recommended course: Building AI Agents & Agentic AI Systems via Microsoft AutoGen

Why: Hands-on with AutoGen, ideal for engineers wanting to build real agent workflows.

2. CrewAI

Designed to orchestrate teams (or “crews”) of agents, CrewAI simplifies multi-agent collaboration, tool use, and large-scale agentic systems.

Recommended course: The Complete Agentic AI Engineering Course

Why: Covers both CrewAI and AutoGen, great for beginners to intermediate, looking at multi-agent frameworks.

3. LangChain

While originally more workflow-oriented, LangChain increasingly supports agentic AI patterns (tool + agent orchestration, memory, chains).

LangChain allows developers to quickly integrate Large Language Models (LLMs) like GPT into applications, enabling features such as natural language understanding, text generation, and AI-powered automation.

It simplifies the complex processes of model integration and accelerates AI app development, making it a go-to framework for creating LLM-powered applications.

Recommended course: LangChain — Develop LLM Powered Applications with LangChain

4. LangGraph

A graph-based framework for modelling multi-agent workflows and dependencies, suited for complexity and scale.

LangGraph builds on LangChain’s capabilities and introduces the concept of AI agents. These agents can autonomously carry out tasks, making decisions based on user input and real-time data.

This makes LangGraph essential for developing more interactive, autonomous systems that can manage workflows or even communicate with other systems.

Recommended course: LangGraph Mastery: Develop LLM Agents with LangGraph

5. LlamaIndex (formerly GPT-Index)

LlamaIndex provides a powerful way to bridge the gap between LLMs and your data. While large models like GPT-4 or LLaMA are incredibly capable, they don’t know your company’s internal documents, private datasets, or custom knowledge bases.

LlamaIndex helps you create pipelines to ingest, index, query, and retrieve data, allowing LLMs to reason over your specific information.

This framework simplifies Retrieval-Augmented Generation (RAG) architectures, making it easier to enhance accuracy, reduce hallucinations, and add context-awareness to your AI applications.

With LlamaIndex, you can rapidly build tools like custom chatbots, search engines, or AI agents that work on your own data — and do it at scale.

Not purely an “agentic” framework, but increasingly used in agent workflows for retrieval, memory, and tool integration.

Recommended course: LlamaIndex Develop LLM-powered apps (Legacy, V0.8.48)

6. Hugging Face Transformers Agents

Hugging Face’s Transformers library provides a simple and powerful API for accessing over 100,000 pre-trained models for tasks like text classification, question answering, translation, summarization, and more.

With seamless support for TensorFlow, PyTorch, and JAX, it has become the go-to toolkit for developers, data scientists, and researchers working in NLP and beyond.

The agentic extension of Hugging Face’s library enables agents that use transformer models in complex workflows.

Recommended course — Learn Hugging Face Bootcamp

7. Semantic Kernel (Microsoft)

Microsoft’s Semantic Kernel (SK) is one of the most enterprise-ready frameworks for building agentic AI systems. It allows developers to easily connect large language models (LLMs) like GPT-4 with real-world tools, data sources, and APIs.

What sets SK apart is its focus on tool-enabled LLMs, function calling, semantic memory, and planning/chaining mechanisms, which make it ideal for orchestrating autonomous workflows in business environments.

It integrates deeply with the .NET ecosystem, Python, and even JavaScript — allowing both backend and full-stack engineers to prototype and deploy intelligent agents quickly.

If you’re working on enterprise applications where AI needs to reason, plan, and act across multiple services, Semantic Kernel is a must-learn framework in 2026.

Here’s a great Udemy course to start: Mastering Semantic Kernel by Creating Projects

8. RASA (Agentic Conversational Agents)

RASA has long been a powerhouse for building contextual chatbots and conversational AI, but in recent years, it has evolved into a more agentic conversational framework.

With RASA Pro and its new open-source components, you can now integrate LLMs, external APIs, and reasoning layers to create hybrid agents by combining deterministic dialogue management with LLM-driven intelligence.

It remains a top choice for teams building customer service agents, voice bots, or AI assistants that must maintain a consistent state and memory across interactions.

Developers can also extend RASA pipelines with retrieval-augmented generation (RAG) and tool execution to make agents more autonomous.

And, if you need a course, a good starting point is: The Complete Course of Rasa Chatbot

9. Atomic Agents

Atomic Agents is an emerging open-source framework designed for decentralized, multi-agent systems. Unlike traditional centralized frameworks, it enables many agents — each with specialized goals — to coordinate tasks in a distributed fashion.

This architecture is particularly useful for complex environments like simulation, large-scale automation, and research collaborations where agents must communicate and negotiate.

The focus on decentralization makes Atomic Agents highly scalable and resilient, positioning it as a promising player in the evolution of agentic AI.

Developers exploring Web3, blockchain, or distributed computing can benefit significantly from learning how Atomic Agents work.

Learn more here: Build GenAI & Multi-Agent Systems Tools for Software Testing

10. Botpress (Agentic Platform)

Botpress has transitioned from a traditional chatbot builder into a full-fledged agentic AI platform. The latest versions allow you to build agents that reason, call tools, and orchestrate multi-step workflows — all within a no-code or low-code interface.

It supports advanced integrations with APIs, databases, and vector stores, allowing you to combine structured logic with LLM-based decision-making.

For organizations and teams looking to deploy scalable, secure AI agents without heavy engineering overhead, Botpress offers the perfect balance of accessibility and sophistication.

Its open-source foundation and plugin ecosystem make it ideal for developers and non-developers alike.

Why This Matters in 2026?

Agentic AI is no longer academic — it is moving into mainstream applications: automation workflows, tool-enabled assistants, and autonomous decision-making systems.

Understanding these frameworks gives you an edge as the AI ecosystem shifts from single-model prompts to multi-agent orchestration, collaboration, and deployment.

Choosing the Right Framework

• For multi-agent orchestration: AutoGen, CrewAI, LangGraph

• For tool-enabled agents & workflows: LangChain, Semantic Kernel

• For distributed/decentralized agents: Atomic Agents, Botpress

• For retrieval/memory-centric agents: LlamaIndex, Hugging Face Agents

Pick the framework that aligns with your goal, stack, and use case — then follow up with one of the Udemy courses listed to deepen your skill.

By the way, if you want to join multiple courses on Udemy, then you can also check out Udemy’s Personal Plan, where you get access to the best of Udemy’s 11000+ courses for a monthly fee of $30.

If you want to join multiple courses, then the Udemy Personal Plan is actually a better deal. You can also try it for free for 7 days to get a feel of it.

So, what are you waiting for? Pick a course, start learning, and join the AI revolution!

Happy Learning!

P. S. — If you are a complete beginner on Agentic AI, then I also recommend you first go through a comprehensive course like The Complete Agentic AI Engineering Course. I highly recommend it to anyone who wants to start with Agentic AI in 2026, and if you need books, you can also check my previous article.

Hello guys, in this rapidly evolving world of artificial intelligence (remember DeepSeek), chatbots and AI agents have become indispensable tools for businesses, developers, and innovators.

From customer support to personal assistants, these intelligent systems are transforming how we interact with technology, be it ChatGPT, GitHub CoPilot or ChatBots used as support personnel, they are not only making support more effective but also providing competitive advantages to the companies.

That’s why more and more companies are looking for experts who can help them to build and set up Chatbots and AI Agents. As per one study, by 2026, the demand for professionals skilled in building and deploying chatbots and AI agents is expected to skyrocket.

If you’re looking to master these technologies, Udemy is one of the best platforms to start your journey, and if you need resources, then you have come to the right place.

By the way, if you are in a hurry and just want to start with building an AI-powered Chatbot, then you can start with building a Messenger ChatBot — using DialogFlow and nodejs. This is a Udemy course that will also teach you, step by step, how to build a Messenger Chatbot that can chat online.

5 Best Udemy Courses to Learn Chatbots and AI Agents in 2026

As a blogger and AI enthusiast who has taken numerous Udemy courses, I’ve curated a list of the 5 best Udemy courses to learn chatbots and AI agents in 2026. These courses cater to beginners, intermediate learners, and advanced practitioners, ensuring there’s something for everyone.

1. Chatbot— The Development Guide 2026 (Beginner + Advanced)

Rating: 4.5/5

Instructor: Eric Roby

Level: Beginner to Intermediate

Why Take This Course?\ Eric Roby is one of Udemy’s most renowned instructors, and this course is a gem for anyone looking to build chatbots from scratch. It focuses on OpenAI, GPT, ChatGPT, DALL-E, and FastAPI to create a ChatBot.

What You’ll Learn:

• Master the modern way to create Chatbots

• How to build rule-based and AI-powered chatbots.

• Integrate GPT and create a Chatbot into your personal application

• Integrate DALLE-2 API to have your Chatbot generate images

Highlights:

• Hands-on projects, including building a customer support chatbot.

• Beginner-friendly explanations with no prior AI experience required.

• Lifetime access to course materials and updates.

Why It’s Relevant in 2026:
As chatbots become more sophisticated, understanding the basics of OpenAI, GPT, ChatGPT, DALL-E, and FastAPI will remain essential. This course provides a strong foundation for building chatbots that can handle complex conversations and integrate seamlessly with popular platforms.

Here is the link to join this course — Chatbot— The Development Guide 2026 (Beginner + Advanced)

2. AI-Agents: Automation & Business with LangChain & LLM Apps

Rating: 4.8/5\ Instructor: Arnold Oberleiter Level: Intermediate to Advanced

Why Take This Course?\ If you’re interested in building AI agents that can learn and adapt, this course is a must. Taught by Arnold, an AI expert, this course will teach you how to build AI Agents with Node.js, Python, JavaScript, LangChain, LangGraph, GPT-4o, Llama, and RAG! Automate tasks, sell software, etc.

What You’ll Learn:

• Basics of AI agents like Autogen, LangChain, LangFlow, Flowise, LangGraph, BabyAGI, CrewAI & more

• All about vector databases, embedding models & retrieval-augmented generation (RAG)

• Building AI agents for games, robotics, and real-world applications.

• Advanced topics like multi-agent systems and transfer learning.

Highlights:

• AI agents with open-source LLMs: Ollama, Llama 3.1 & more

• Local Microsoft Copilot with Vision as an AI agent in Python

• Taught by one of the most respected figures in AI.

Here is the link to join this course — AI-Agents: Automation & Business with LangChain & LLM Apps

3. ChatGPT Complete Guide: Learn Generative AI, ChatGPT & More

Rating: 4.7/5

Instructor: Julian Melanson and Benza Maman

Level: Intermediate

Why Take This Course?\ Julian Melanson and Benza Maman are seasoned AI instructors, and this course focuses on leveraging OpenAI’s GPT models (like ChatGPT) to build advanced chatbots. It’s perfect for developers who want to create chatbots that can understand context, generate human-like responses, and handle complex queries.

What You’ll Learn:

• How to use OpenAI’s GPT models for chatbot development.

• Fine-tuning GPT models for specific use cases.

• Building multi-turn conversational agents.

Highlights:

• Hands-on projects, including building a chatbot for e-commerce.

• Practical tips for optimizing chatbot performance.

• Access to the OpenAI API for experimentation.

Why It’s Relevant in 2026:

GPT models are setting new standards for conversational AI. By learning how to harness their power, you’ll be able to build chatbots that deliver exceptional user experiences.

Here is the link to join this course — ChatGPT Complete Guide: Learn Generative AI, ChatGPT & More

4. Master Google Dialogflow ES: Build Smart Chatbots

Rating: 4.7/5

Instructor: Raj Kapadia
Level: Beginner to Intermediate

Why Take This Course? Dialogflow is one of the most popular tools for building AI chatbots, and this course provides a comprehensive introduction to it. Whether you’re building chatbots for Google Assistant or other platforms, this course will teach you how to design, develop, and deploy conversational agents.

What You’ll Learn:

• Basics of Dialogflow and its components.

• Designing conversational flows and intents.

• Integrating chatbots with Google Assistant and other platforms.

• Using fulfillment to connect chatbots to backend systems.

Highlights:

• Step-by-step tutorials for building chatbots.

• Real-world examples, including a weather bot and a restaurant booking bot.

• Beginner-friendly with no prior coding experience required.

Why It’s Relevant in 2026: Dialogflow continues to be a leading platform for chatbot development, especially for voice-based assistants. Mastering it will give you a competitive edge in the chatbot industry.

Here is the link to join this course — Master Google Dialogflow ES: Build Smart Chatbots

5. The Complete AI Agent— All Levels — Build An AI Assistant

Rating: 4.5/5
Instructor: Clarion North
Level: Intermediate

Why Take This Course?
This course is tailored for professionals who want to use AI agents to automate business workflows. It combines robotic process automation (RPA) with AI to teach you how to build intelligent systems that can handle repetitive tasks, analyze data, and make decisions.

What You’ll Learn:

• Basics of RPA and AI integration.

• Building AI agents for tasks like data entry and report generation.

• Using tools like UiPath and Python for automation.

• Deploying AI agents in real-world business scenarios.

Highlights:

• Practical projects, including automating invoice processing.

• Focus on business applications and ROI.

• Taught by a top-rated instructor with a focus on real-world skills.

Why It’s Relevant in 2026: As businesses increasingly adopt AI for automation, professionals who can build and deploy AI agents will be in high demand. This course equips you with the skills to meet that demand.

Here is the link to join this AI Agent course — The Complete AI Agent — — All Levels — Build An AI Assistant

Why Learn Chatbots and AI Agents in 2026?

The rise of chatbots and AI agents is reshaping industries and creating new opportunities for innovation.

Here’s why you should invest in learning these technologies:

1. Growing Demand: Businesses across sectors are adopting chatbots and AI agents to improve customer engagement and streamline operations.

2. Career Opportunities: Skills in chatbot development and AI agents are highly sought after, with roles like AI engineer, NLP specialist, and automation consultant offering lucrative salaries.

3. Innovation Potential: Chatbots and AI agents are at the forefront of AI innovation, enabling new applications in healthcare, education, finance, and more.

4. Personal Projects: Whether you’re building a personal assistant or a niche chatbot, these skills empower you to bring your ideas to life.

If you just want to start with building ChatBots and AI Agents now, then I highly recommend you join Build, Train & Sell AI Chatbots [No-code x Chat GPT]

Final Thoughts

That’s all about the best Udemy courses to learn building Chatbots and AI Agents in 2026. The courses listed above are some of the best on Udemy for learning chatbots and AI agents in 2026.

Whether you’re a beginner looking to build your first chatbot or an experienced developer exploring advanced AI agents, these courses will provide the knowledge and hands-on experience you need to succeed.

By the way, if you want to join multiple courses on Udemy, then you can also check out Udemy’s Personal Plan, where you get access to the best of Udemy’s 11000+ courses for a monthly fee of $30. If you want to join multiple courses, then the Udemy Personal Plan is actually a better deal. You can also try it for free for 7 days to get a feel of it.

So, what are you waiting for? Pick a course, start learning, and join the AI revolution!

Happy Learning!

Thanks for reading this article so far. If you like these best AI and chatbots courses, then please share them with your friends and colleagues. If you have any questions or feedback, please drop a note.

P. S. — If you want to learn ChatGPT but are looking for a free online course, you can also check out ChatGPT Masterclass: A Complete ChatGPT Zero to Hero! udemy course. It’s completely free; all you need is a free Udemy account to join this course.

Hello guys, if you are preparing for system design interviews or simply want to improve your software architecture skills, chances are you’ve already come across names like ByteByteGo, NeetCode, and Educative.

But which of these is actually worth your time and money?

Let’s break it down together — I’ll also share inputs from people (including myself) who’ve gone through these resources or prepared seriously for system design interviews.

Why System Design Courses Matter?

System design interviews are tough. Unlike coding rounds, they test your ability to think at scale, design for high availability, consistency, fault tolerance, and make smart trade-offs.

No matter how good you are at coding, if you have not seen these concepts applied at scale (like millions of users), it’s easy to get stuck.

That’s why having a good structured resource can help — but picking the right course makes all the difference.

ByteByteGo — The Visual Powerhouse

ByteByteGo, created by Alex Xu (author of System Design Interview — An Insider’s Guide), is like a digital extension of his books. It is highly visual — if you love diagrams, flowcharts, and step-by-step illustrations, this platform will suit you well.

Pros:

1. Extremely clean diagrams that explain complex topics in a simple way

2. Covers popular system design problems like YouTube, WhatsApp, URL shortener, etc.

3. Regularly updated content — includes OOP Design, Generative AI Design, ML System Design, and Coding Patterns

4. Exclusive community and mock interview opportunities

Cons:

❌ Subscription model may feel pricey for some
❌ Focuses more on breadth of systems rather than an extreme deep-dive into internals (but still solid for interviews)

My take:

If you are a visual learner and like things presented clearly and concisely, ByteByteGo is worth considering, especially during the interview preparation phase.

And, with 50% discount they are offering now, it’s really a no-brainer to join this site. I have recommended it to both my friends and my readers who are working as software engineers, tech leads, and senior engineers.

Here is the link to get a discount — ByteByteGo 50% OFF NOW

2. NeetCode — A Beginner-Friendly Choice

If you are just getting started with system design (say, you’re a fresher or someone with limited large-scale project exposure), NeetCode’s system design series is surprisingly good.

Pros:

✅ NeetCode keeps it simple and beginner-friendly
✅ Affordable or free (depending on the resource)
✅ Good as a first pass to build a basic understanding

Cons:

❌ He is relatively new to system design, so the content may not cover extremely advanced scenarios
❌ Doesn’t go deep into trade-offs and real-world production scale challenges

My take:

NeetCode is a good starting point. If you’re a fresher or switching to backend/architecture, this gives you the confidence to start tackling basic system design problems.

But don’t stop here — you will want to supplement it later with books like DDIA or System Design Interview — An Insider’s Guide by Alex Xu.

3. Educative’s Grokking the System Design Interview — The Classic

Educative’s Grokking the System Design course is probably one of the most popular system design resources out there — many candidates swear by it.

Pros:

✅ Covers all standard interview problems in a clear, structured way
✅ Good balance of theory and practice
✅ Text-based — no fluff, straight to the point
✅ Very beginner-friendly

Cons:

❌ Price can feel high unless you have a subscription
❌ Some of the content feels dated now (though still very relevant for interviews)

My take:

If you want something structured and proven — especially if you are aiming at FAANG / top tech interviews — Grokking System Design interview course on Educative is a safe bet. Pair it with additional study for modern architectures (e.g., Gen AI systems, ML pipelines).

By the way, you would need an Educative subscription to join this course, which costs around $14.99 but also provides access to more than 1000+ courses, projects, and cloud labs to learn in-demand tech skills, including web development. You can also use 7 days free trial to get this course for FREE.

What About Books?

Many experienced engineers actually skip courses entirely and focus on books:

👉 System Design Interview by Alex Xu — Great for learning patterns and preparing for common interview questions
👉 Designing Data-Intensive Applications by Martin Kleppmann — For solid fundamentals in distributed systems

A common approach is:
📌 First read Alex Xu’s book (or ByteByteGo if you like digital)
📌 Deep dive into Kleppmann (chapters 4–9)
📌 Re-read Alex Xu’s material or revisit problems in your course

Other Options You Can Explore

If you want alternatives:

• YouTube (free but unstructured) — Gaurav Sen, ByteByteGo channel

• Codemia.io/System Design School / BugFree.ai / Exponent — These offer more practice, mentorship + mock interview focus, but costlier

• Build Projects — Ultimately, nothing beats hands-on design. Pick a project like building a URL shortener, notification system, or mini-YouTube clone and design it end-to-end.

Final Thoughts — Which One to Pick?

✨ If you’re a fresher/beginner: Start with NeetCode or Grokking System Design course — keep it simple, build confidence
✨ If you’re a visual learner / mid-senior level: ByteByteGo is excellent — especially if you want to go beyond standard problems
✨ If you want depth: Combine any of the above with DDIA (book) and do projects

Pro tip: Don’t just consume courses — practice by writing out your designs, discussing them with peers, and doing mock interviews.

All the best for your System Design Interviews. If you have any doubts or questions, feel free to ask in the comments.

P. S. — If you just want to do one thing at this moment, go join ByteByteGo and start learning System Design and Coding Interview concepts, you will thank me later. It’s one of the most comprehensive resource for system deisgn interview now covering OOP Design, ML Design, Gen AI Design and traditional System Design.

Hello guys, Machine Learning and Deep Learning aren’t future technologies anymore — they’re right now technologies reshaping every industry.

In 2026, companies aren’t asking “should we use AI?” They’re asking, “Why aren’t we using more AI?” The demand for ML engineers has exploded. Salaries are skyrocketing. Opportunities are everywhere.

But here’s the challenge: ML and Deep Learning are complex. There’s math involved. There are frameworks. Some concepts don’t click on the first attempt. You need instruction from people who understand not just the theory, but how to apply it in practice.

That’s where Coursera comes in. Coursera partners with the world’s best AI experts. When you take a course from Andrew Ng (literally the founder of AI education), you’re learning from someone who’s shaped how millions learn ML.

In 2026, the question isn’t whether you should learn ML. It’s when you’ll start. Let me show you the five best Coursera courses and specializations to get you there.

5 Best Machine Learning and Deep Learning Courses and Certificates from Coursera for 2026

Without any further ado, here are the top 5 Machine Learning and Deep Learning Courses and certificates you can join on Coursera in 2026

1. Machine Learning Specialization by Andrew Ng

This is where most people should start. Andrew Ng designed this specialization specifically to help people break into AI with no prerequisites.

What You’ll Learn:

• Machine learning fundamentals from scratch

• Supervised learning (regression, classification)

• Unsupervised learning (clustering, anomaly detection)

• Practical machine learning workflow

• How to build and train ML models

• Evaluation metrics and model selection

• Real-world problem-solving with ML

• Python and scikit-learn implementation

Why It’s Essential: Andrew doesn’t assume you know anything. He starts from zero and builds systematically. By the end, you understand ML deeply — not just how to use libraries, but why things work.

The specialization includes 3 courses that build on each other perfectly. You’re not jumping around randomly; you’re following a well-designed learning path.

Best For: Beginners completely new to ML, career changers entering AI, and anyone wanting a solid foundation.

Time Commitment: 3–4 months at 5–7 hours/week

Enrollment: 715,414 students have already taken this — a testimonial to its quality

Progression: Start here before deep learning. This gives you the foundation that makes Deep Learning click.

Here is the link to join — Take Machine Learning Specialization

2. Deep Learning Specialization by Andrew Ng

After Machine Learning Specialization, this is your natural next step. The Deep Learning Specialization is the most comprehensive deep learning program available.

What You’ll Learn:

• Neural network fundamentals and intuition

• Building and training deep neural networks

• Convolutional Neural Networks (CNNs) for computer vision

• Recurrent Neural Networks (RNNs) for sequences

• Natural Language Processing with Deep Learning

• Transformer architectures and attention mechanisms

• Advanced optimization techniques

• Hyperparameter tuning and regularization

• Practical deep learning projects

Why It’s Transformative: This specialization was updated with cutting-edge techniques like transformers and attention mechanisms. You’re learning what’s actually used in production, not outdated material.

The 5-course series takes you from basics to state-of-the-art. By the end, you can build production-ready deep learning systems.

Best For: ML engineers ready to go deeper, computer vision specialists, NLP practitioners, and researchers.

Time Commitment: 4–6 months at 8–10 hours/week

Enrollment: 956,905 students — the most popular deep learning specialization globally

Student Rating: 4.9/5 (136,900+ reviews)

Progression: Take Machine Learning Specialization first, then this. They’re designed to flow together.

Here is the link to join — Take Deep Learning Specialization

3. IBM AI Engineering Professional Certificate

IBM’s certificate is designed specifically for career acceleration. It’s not academic — it’s practical, focused on getting you employed.

What You’ll Learn:

• Machine learning algorithms and their applications

• Deep learning frameworks (TensorFlow, Keras, PyTorch)

• Computer vision and NLP applications

• Building and deploying ML models

• Real-world AI engineering projects

• Industry best practices

• Resume and interview preparation

Why It’s Valuable: IBM designed this certificate knowing exactly what employers want. Every project is portfolio-ready. You’ll have tangible work to show employers.

The focus on practical deployment means you learn not just how to build models, but how to put them in production — crucial knowledge most ML courses skip.

Best For: Career changers wanting to land an AI engineering role, developers wanting practical ML skills, and people with limited time.

Time Commitment: 4–5 months at 5–7 hours/week

Enrollment: 173,999 professionals have enrolled

Job Outcomes: IBM tracks employment outcomes — people completing this certificate get job offers

Career Impact: This certificate is recognized by employers. It carries weight on your resume.

Here is the link to join this course — Take IBM AI Engineering Professional Certificate

4. IBM Deep Learning with PyTorch, Keras, and TensorFlow Professional Certificate

If you want to go deep into the tools and frameworks, this certificate is your path. It’s hands-on, framework-heavy, and practical.

What You’ll Learn:

• PyTorch fundamentals and advanced techniques

• Keras API and TensorFlow ecosystem

• Building CNNs from scratch

• Transfer learning and fine-tuning

• Building and training RNNs and LSTMs

• Natural Language Processing with Deep Learning

• Practical deep learning projects

• Deployment and production considerations

Why It’s Different: Most deep learning courses teach concepts, then show implementations. This course teaches frameworks as concepts. You learn PyTorch, Keras, and TensorFlow deeply.

You build actual production-grade models using real frameworks. By the end, you can pick up any deep learning project and execute it.

Best For: Python developers wanting deep learning skills, engineers needing framework expertise, and researchers needing implementation skills.

Time Commitment: 3–4 months at 8–10 hours/week

Enrollment: 10,494 professionals — smaller, focused cohort

Framework Coverage: PyTorch, Keras, TensorFlow — the three most important frameworks

Practical Focus: Every module includes hands-on projects using real datasets

Here is the link to join this certification — IBM Deep Learning with PyTorch, Keras, and TensorFlow Professional Certificate

5. Data Analytics and Deep Learning Specialization

This specialization takes a unique angle — it combines data analytics with deep learning, showing you how real-world data flows into deep learning pipelines.

What You’ll Learn:

• Advanced data preprocessing and cleaning

• Big data technologies and tools

• Exploratory data analysis

• Building predictive models with deep learning

• Analyzing complex datasets

• Data visualization techniques

• End-to-end ML pipeline development

• Real-world case studies

Why It’s Valuable: Most people learn deep learning in isolation. This specialization shows you the complete journey: data collection → preprocessing → analysis → modeling → deployment.

You learn that deep learning is just the final piece of a much larger system. Understanding the complete pipeline makes you a 10x better engineer.

Best For: Data analysts transitioning to deep learning, engineers building end-to-end ML systems, and people wanting the complete ML lifecycle.

Time Commitment: 4–5 months at 6–8 hours/week

Unique Angle: Data-first approach instead of just algorithm-focused

Real-World Focus: Emphasis on working with messy, real-world data

Here is the link to join this course — Data Analytics and Deep Learning Specialization

My Recommended Learning Path

Path 1: Complete ML/DL Mastery (6–12 months)

1. Months 1–4: Machine Learning Specialization
   Build your foundation

2. Months 5–10: Deep Learning Specialization
   Go deep into neural networks

3. Months 11–12: IBM Deep Learning with Frameworks
   Master implementation

Path 2: Fast-Track to Employment (4–5 months)

1. Month 1: Machine Learning Specialization
   Get fundamentals fast

2. Months 2–4: IBM AI Engineering Professional Certificate
   Build a portfolio and get job-ready

Path 3: Data-Centric Learning (5–6 months)

1. Months 1–3: Data Analytics and Deep Learning Specialization
   Learn the complete pipeline

2. Months 4–6: Deep Learning Specialization
   Deepen deep learning knowledge

Why These 5 Courses and Professional Certificates Stand Out?

1. Taught by Andrew Ng — The person who literally created modern AI education (ML and DL specializations)

2. Taught by IBM — Industry leader with real-world ML/DL applications (IBM certificates)

3. Practical Focus — Every course includes real projects with real data

4. Job-Ready — Certificates and specializations recognized by employers

5. Updated for 2026 — Including transformers, latest techniques, modern frameworks

6. Flexible — Learn at your own pace, on your own schedule

7. Affordable — Especially with the Coursera Plus.

Unlock Everything With Coursera Plus

Here’s the game-changer: instead of buying individual courses, get Coursera Plus.

What You Get:

• All 3,000+ Coursera courses (unlimited)

• All specializations and professional certificates

• Guided projects

• Hands-on labs

• One-year subscription

The Math:

• Machine Learning Specialization alone: $39/month (~$156 for 4 months)

• Deep Learning Specialization: $39/month (~$234 for 6 months)

• IBM AI Certificate: $39/month (~$156 for 4 months)

Total if bought separately: $546+

Coursera Plus for $399)

You pay $239.40 and get unlimited access to all of these courses plus 2,995 others. You could take 10 courses, and it pays for itself.

The Bottom Line

That’s all about the 5 best Coursera courses, specializations, and professional certificates to learn Machine Learning and Deep Learning in 2026. Machine Learning and Deep Learning are the most important skills you can learn in 2026.

These five Coursera courses give you a complete pathway from absolute beginner to job-ready professional:

1. Machine Learning Specialization — Build your foundation

2. Deep Learning Specialization — Master neural networks

3. IBM AI Engineering — Get employed fast

4. IBM Deep Learning Frameworks — Master implementation

5. Data Analytics + DL — Understand the complete pipeline

Pick a path based on your goal. Commit to it. Actually do the projects. In 6 months, you’ll be miles ahead of developers who are still “thinking about” learning ML.

Your best time to start was yesterday. Your second-best time is today.

Start Your ML Journey

• Machine Learning Specialization — Best starting point

• Deep Learning Specialization — Frontier of AI

• IBM AI Engineering Certificate — Get hired

• Coursera Plus at 40% OFF — Unlimited access to all courses

The future of AI belongs to people who learn it now. Make sure you’re one of them.

Start with Coursera Plus today and get unlimited access to all these courses plus thousands more.

Happy learning!

P. S. — By the way, if you find Coursera courses useful, which they are because they are created by reputed companies and universities around the world, I suggest you join Coursera Plus, a subscription plan from Coursera that gives you unlimited access to their most popular courses, specializations, professional certificates, and guided projects. It costs around $399/year, but its complete worth your money as you get unlimited certificates.

Preparing for System Design Interview? ByteByteGo can help you. They are also offering a rare 50% discount now on their lifetime plan

Hello folks, System design interviews are a crucial part of technical hiring, especially for senior engineering roles. When preparing for these interviews, two of the most popular resources are ByteByteGo by Alex Xu and Grokking the System Design Interview by DesignGurus.io.

Being a programmer and author, I often receive questions like Which one is better? Should I join ByteByteGo or take the Grokking the System Design interview course?

Well, both have established themselves as leading study materials for system design, but they offer different approaches.

In this comparison, we will analyze their content, teaching style, suitability for interview preparation, pricing, and other relevant factors to help you choose the one that best fits your learning needs.

So what are you waiting for? Let’s start with

By the way, I just wanted to inform you that ByteByteGo is offering a 50% discount now on their annual and lifetime plan. I just bought the lifetime plan.

I highly recommend it to any developer who is looking for a chance. This plan provides the best value, as with the cost of 2 years, you get access for the whole life and get all content upgrades for free.

Here is the link to get a discount - 50% OFF on ByteByteGo Lifetime Plan

ByteByteGo vs DesignGurus.io’s Grokking The System Design Interview Course

In this article, I will compare both ByteByteGo and Designgurs.io’s popular “Grokking the System Design Interview” course on various parameters like

1. Content

2. Teach Style

3. Interview Preparation Focus

4. Time commitment

5. Price

6. Additional Feature etc

After this, you will be in a better position to choose which resource is better suited for your needs and why.

1. Content Coverage

ByteByteGo by Alex Xu: ByteByteGo primarily focuses on in-depth explanations of real-world system design scenarios, offering comprehensive coverage of topics like scalability, microservices, caching, data partitioning, and more.

Alex Xu is known for his structured and detailed approach, breaking down complex topics into digestible pieces. The content is rich with high-resolution diagrams, step-by-step breakdowns, and clear trade-off discussions.

ByteByteGo is often updated with new and evolving system design concepts and technologies.

Grokking the System Design Interview by DesignGurus.io: Grokking the System Design Interview takes a case-based approach, providing a variety of system design problems that are frequently asked in interviews.

It covers fundamental system design concepts such as load balancers, distributed systems, database sharding, and message queues, but its focus is more on helping learners tackle specific system design questions.

Each problem is solved in a clear, systematic way, giving learners a blueprint for how to approach similar questions in interviews.

Verdict: ByteByteGo covers a broader range of system design concepts, while Grokking is more problem-oriented, making it a better choice for those who want to focus on practice.

2. Teaching Style

ByteByteGo takes a more academic approach, with a heavy focus on theory and trade-offs. Alex Xu explains not only how systems are designed but also why certain architectural decisions are made.

He includes extensive diagrams and deep dives into scalability, consistency, and fault tolerance. This method is ideal for learners who prefer a more structured and theory-driven learning process.

Grokking the System Design Interview: Grokking is all about practicality. It presents common interview problems like how to design Twitter or X.com?, breaks them down, and walks you through the design process.

The explanations are direct and aimed at guiding you through interview-style problem-solving. This course is designed for people who prefer hands-on learning with a clear focus on interview preparation.

Verdict: ByteByteGo is better suited for those who want a deep understanding of system design principles, while Grokking is perfect for those who want to practice solving interview questions.

3. Interview Preparation Focus

While ByteByteGo offers comprehensive coverage of system design concepts, it’s not strictly limited to interview preparation.

The ByteByteGo course and website are actually a digital version of Alex’s popular books System Design Interview --- An Insider’s Guide and System Design Interview --- An Insider’s Guide: Volume 2

The book goes beyond just preparing you for interviews and aims to help you build a solid foundation in system design that can be applied in your day-to-day work as a software engineer.

Grokking the System Design Interview
As the name suggests, Grokking is laser-focused on interview preparation.

Every section is designed to help you navigate common system design interview questions, giving you step-by-step guidance on how to structure your answers in an interview setting.

It also shares a nice System Design Master Template, which you can use to structure your answer better

**Verdict: If your primary goal is to ace system design interviews, Grokking might be the better option. ByteByteGo*, on the other hand, provides deeper long-term knowledge, which is valuable for both interviews and practical system design work.

4. Time Commitment

Given its comprehensive nature, ByteByteGo requires a greater time investment. The material is more detailed, and to get the most out of it, you’ll need to spend a significant amount of time digesting the theory and working through the examples.

On the other hand, the Grokking course is designed to be more concise. You can go through individual case studies in a relatively short amount of time, making it easier to fit into a busy schedule.

This is especially useful for those who are cramming for an upcoming interview.

Verdict: If you’re short on time, Grokking’s focused and concise approach is more manageable. ByteByteGo is a better choice if you have more time to dedicate to learning.

5. Price

ByteByteGo is available in a single subscription model called an Annual or lifetime plan, which costs around US $199, which is not costly compared to other online resources.

For example, Codemia.io, another Leetcode-style System design practice platform, is $99 per year, though they are offering a discount now, and it comes around the same $59.9

With an Annual pass comes high-resolution diagrams, detailed explanations, and lifetime access to updates. It also comes with an exclusive Discord community where you can discuss your doubts, get help, and conduct mock interviews with buddies for better preparation.

Another great point is that it contains the content from both System Design Interview -An Insider’s Guide and System Design Interview --- An Insider’s Guide: Volume 2, as well as 7 other books on OOP Design, ML System Design, and Gen AI System Design, which individually cost around the same amount.

So the course is actually a good deal as you are getting content from both books + new content + discord channel + mock interviews buddy with the cost of one book. They are also offering a 50% discount now.

Here is the link to join and learn more - Join ByteByteGo Now

Grokking the System Design Interview:

When it comes to joining Grokking the System Design Interview, you have multiple choices available. For example, you can get a single course for a lifetime for $89, or you can get a monthly pass for just $16.45 per month.

All Grokking courses on DesignGurus.io are available via a subscription-based model or as a bundle.

For example, if you like these Grokking interview courses from DesignGurus.io, you can get all of them for a big discount by purchasing their All course bundle.

This bundle includes their most popular Grokking courses like Grokking System Design Interview, OOP Design, Coding Patterns, as well as Grokking Dynamic Programming Interview. You can also use code GURU to get a 30% discount.

It can be somewhat pricey for those on a budget, but it offers practical value, especially if you’re looking for a quick, interview-focused resource.

Here is the link to join this course - Grokking the System Design Interview

Verdict: Both resources require some financial investment, but ByteByteGo offers more long-term value, while Grokking is a more immediate investment for interview preparation.

6. Additional Features

One of the key features of ByteByteGo is its use of high-quality, high-resolution images and diagrams.

For example, here is a nice System Design Blueprint from ByteByteGo, which is a great visual guide for System design topics

The book and course place a strong emphasis on visual learning, which can be helpful for complex system design topics.

Additionally, Alex Xu frequently updates the content to reflect new technologies and industry trends.

Grokking the System Design Interview:\
Grokking provides a clear and structured way to solve system design problems, but lacks the high-resolution visuals that make ByteByteGo stand out.

However, the problem-solving approach is very practical and easy to follow, which is its key strength.

Though it does have quite useful diagrams, like shown below,w which also aids in explaining key concepts like API Gateway vs Load Balancer

Verdict: If visuals and constant updates are important to you, ByteByteGo excels in that department. If you want a straight-to-the-point, problem-solving approach, Grokking is your go-to.

ByteByteGo vs DesignGurus.io? Which One Should You Choose for System Design Interview Prep in 2026?

Ultimately, the decision between ByteByteGo and Grokking the System Design Interview depends on your goals and learning style.

You should choose ByteByteGo if you are looking for a deeper understanding of system design concepts and you prefer learning through detailed theory and diagrams.

It is a better choice for engineers who want to apply system design knowledge beyond interviews.

Similarly, choose Grokking the System Design Interview** if you are focused on preparing specifically for system design interviews and prefer learning through problem-solving and step-by-step guides.

It’s ideal for those who are cramming for an upcoming interview and need quick, actionable insights.

Why not both?

Well, combining both resources can be the best strategy. You can use Grokking the System Design Interview course for fast-paced, interview-specific learning, and ByteByteGo to deepen your overall understanding of system design.

By learning from multiple sources, you not only prepare for interviews but also become a stronger, more well-rounded engineer.

In the end, the more diverse your resources, the better equipped you’ll be to handle the vast and complex world of system design.

All the best for your System design interview

Thanks for reading this article so far. If you like this ByteByteGo vs DesignGurus.io article, then please share it with your friends and colleagues. If you have any questions, feel free to ask in the comments.

ByteByteGo also provides a free system design PDF, which is gold; you can also download it here

P. S. - By the way, DesignGurus.io also has many other Grokking courses to prepare for essential coding interview topics like OOP Design, System Design, Dynamic Programming, etc., and you can get access to all of their courses for a big discount by joining their All course bundle. You can also use code GURU to get 30% discount.

Preparing for System Design Interview? ByteByteGo can help you. They are also offering a rare 50% discount now on their lifetime plan

Hello guys, Artificial Intelligence is no longer a domain reserved for Python developers or machine learning experts.

Thanks to tools like Spring AI, Java developers can now build intelligent applications with powerful large language models (LLMs), retrieval-augmented generation (RAG), agentic workflows, and multimodal inputs — all using the familiar Spring Boot ecosystem.

In 2026, knowing how to integrate AI into your Java stack isn’t just a nice-to-have — it’s a strategic career move.

Whether you’re an enterprise engineer, a solo developer, or someone working on cutting-edge GenAI apps, learning Spring AI will let you bring LLMs like GPT, Claude, and Ollama directly into your Java codebase.

To help you get started, I’ve curated the 7 best Udemy courses to learn Spring AI in 2026. These courses range from beginner-friendly tutorials to advanced projects involving autonomous agents and multi-modal interfaces.

7 Best Udemy Courses to learn Spring AI in 2026

Without any further ado, here are the best online courses you can join to learn Spring AI on Udemy in 2026. These courses are for both beginners and experienced developers who are looking for ways to integrate LLM and AI capabilities in Java applications.

1. Spring AI: Beginner to Guru (Udemy)

If you are just starting with both AI and Spring AI then this is one of the best AI courses for Java developers.

Created by one of my favorite Spring teachers and instructor John Thompson, this course is built in the same spirit as the popular “Spring Boot: Beginner to Guru,” one of the most popular course to learn Spring Boot.

This Spring AI edition teaches you how to connect your Spring Boot applications with OpenAI’s GPT models. The course is the ultimate starting point for any Java developer looking to break into AI.

You’ll learn how to:

• Set up Spring AI in a Java app

• Build conversational apps using ChatGPT

• Integrate prompt engineering

• Handle structured outputs and tokens

Highly recommended if you’re new to LLMs and want to start slow with solid fundamentals. Talking about social proof, more than 6,351 students have already joined this course and it has an amazing rating.

Here is the link to join this course — Spring AI: Beginner to Guru

2. Mastering Spring AI: Build AI with Java (Udemy)

Bestseller | 774 students

If you’ve already got a few Spring Boot projects under your belt, this intermediate course will help you apply Spring AI in real-world enterprise apps.

What makes this course stand out is its emphasis on enterprise-grade architecture:

• Model chaining

• Prompt templating

• Multi-model switching (OpenAI, HuggingFace, Ollama)

• Managing context and memory

This is one of the most hands-on, practical courses for Java engineers working on production software.

Here is the link to join this course — Mastering Spring AI: Build AI with Java

3. Spring AI for Beginners: Build GenAI LLM Apps in Easy Steps

Highest Rated | 1,511 students

If you like step-by-step instructions, minimal fluff, and practical walkthroughs — this course is for you. Created by one of my favorite authors, Bharath Thippireddy, this is a great hands-on course to learn Spring AI in 2026.

You’ll learn:

• How to make your first LLM-powered Spring Boot app

• Connect OpenAI/GPT APIs

• Build a small chatbot and content generator

• Avoid common pitfalls when integrating AI with Java

It’s short, clear, and highly rated by learners who are starting from scratch.

Here is the link to join this course — Spring AI for Beginners: Build GenAI LLM Apps in Easy Steps

4. Master Generative AI with Java and Spring Boot | Spring AI

This is another Udemy course you can join to learn Spring AI in 2026. This course is great for understanding the full development lifecycle of AI apps using Java and Spring Boot. It covers:

• Prompt design

• API integration

• Memory management

• LLMOps and continuous improvement

You’ll work with OpenAI and HuggingFace models, use Spring configuration best practices, and get exposure to versioning your AI stack for updates.

Talking about social proof, more than 3,333 students have already joined this course, and it has got on average 4.3 rating which is great.

Here is the link to join this course— Master Generative AI with Java and Spring Boot | Spring AI

5. Build AI Apps with Spring AI, OpenAI, Ollama & Spring Boot

One of the most exciting trends in 2026 is multimodal AI — where models don’t just handle text but also images, PDFs, and tabular data. This Udemy course introduces that by:

• Using Ollama for lightweight on-device LLMs

• Building RAG pipelines

• Implementing tool-calling and vector search

• Generating responses based on uploaded documents

It’s hands-on and ideal for developers building private AI apps or internal tools.

Here is the link to join this course — Build AI Apps with Spring AI, OpenAI, Ollama & Spring Boot

6. Spring AI: Creating Workflows, Agents, and Parsing Data (Udemy)

Hot & New | 673 students

This is one of the few Udemy courses focused entirely on building agentic workflows in Java.

Topics include:

• Creating autonomous agents using Spring AI

• Workflow orchestration with memory, tools, and decision trees

• Parsing structured and semi-structured data

• Running agent pipelines end-to-end

If you’re interested in using LangGraph-like capabilities or building AutoGPT-style apps in Java, this course will get you there faster.

Here is the link to join this course — Spring AI: Creating Workflows, Agents, and Parsing Data

7. Spring AI — GenAI with Telusko (Udemy)

If you are looking for a Spring AI course that also covers Generative A,I then this course is for you. Telusko or Navin Reddy is one of the most popular YouTube instructors in the Java space, and in this course, he dives into:

• Using Spring AI with OpenAI, Ollama, and vector DBs

• Building AI-powered Spring Boot apps from scratch

• Creating multi-modal, chat-enabled web applications

• Combining RAG, embeddings, and local models

It’s new, relevant, and very approachable for those who like project-driven learning. Talking about social proof, this is a new course, but more than 1,015 students have already joined the course, and it has a 4.9 out of 5 rating, which is amazing.

Here is the link to join this course — Spring AI— GenAI with Telusko

Conclusion

That’s all about the 7 best Udemy courses to learn Spring AI in 2026. Spring AI is quickly becoming the go-to toolkit for Java developers building AI-native applications in 2026.

With seamless integration with OpenAI, HuggingFace, Ollama, and emerging frameworks like LangGraph, it allows developers to stay in the Java ecosystem while working on cutting-edge GenAI projects.

Whether you’re starting from scratch or want to build enterprise-grade AI agents, these top 7 Udemy courses will accelerate your learning, save hours of trial and error, and help you master the future of Java development.

By the way, if you want to join multiple courses on Udemy, it may be worth getting a Udemy Personal Plan, which will give instant access to more than 11,000 top-quality Udemy courses for just $30 a month.

If you have a lot of time and want to save money, the Udemy Personal Plan will be perfect for you.

Let’s build the future, one AI agent at a time.

Thanks for reading this article so far. If you find these Udemy Courses for learning Spring AI from scratch, including tools and libraries, then please share them with your friends and colleagues. If you have any questions or feedback, please drop a note.

P. S. — If you want to learn from books and looking for best AI and LLM Books then I highly recommend you to read AI Engineering by Chip Huyen and The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne, both of them are great books and my personal favorites. They are also highly recommend on Redditt and HN.

From generating human-like text to creating stunning visuals and even writing code, generative AI is at the forefront of technological innovation.

As more businesses embrace these technologies, the demand for skilled professionals who understand generative AI and tools like ChatGPT is skyrocketing.

Whether you’re a software engineer looking to upskill, a student exploring AI, or a business professional aiming to leverage these tools for competitive advantage, learning generative AI is essential in 2026.

Agentic AI Engineering Course by Paul Iustzin

If you’re serious about Agentic AI, this is the course you should check out.

Authored by Paul Iusztin (bestselling LLM Engineer’s Handbook author), the Agentic AI Engineering course takes you from fundamentals to production-ready agents with real-world projects, evaluation frameworks, and deployment pipelines.

Perfect for engineers who want to move beyond demos.

Here is the link to join this course - Agentic AI Engineering by Paul Iusztin

If you want to learn Generative AI and ChatGPT in 2026 and are looking for the best resources, then you have come to the right place.

Today, I am going to share the best interactive courses to learn Generative AI and ChatGPT in 2026, and when it comes to finding interactive courses, Educative.io is the first name that comes to mind.

Educative.io, with its interactive learning platform and text-based courses, offers an excellent way to build expertise in generative AI and ChatGPT.

In this article, we’ll explore the 5 best Educative.io courses for learning generative AI and ChatGPT, helping you find the right course to kickstart or accelerate your journey.

5 Best Generative AI and ChatGPT Courses on Educative.io in 2026

Without any further ado, here are the best online courses you can join to learn Generative AI on Educative.io in 2026:

1. Generative AI: From Theory to Product Launch

This course is a perfect starting point for anyone curious about generative AI. It explains the fundamental concepts behind generative models like GANs (Generative Adversarial Networks) and transformers.

The course takes you through real-world applications such as generating images, text, and music.

Key Highlights:

• Basics of generative AI and its applications

• Introduction to GANs and transformers

• Hands-on coding exercises to build foundational skills

Best for: Absolute beginners in AI and generative models

Here is the link to join this course — Generative AI: From Theory to Product Launch

2. Master ChatGPT Prompt Engineering

Prompt Engineering is one of the best skills to learn in 2026 because it is the skill that allows you to make the most of AI tools like ChatGPT and Google Gemini.

This course will teach you the fundamentals of Prompt Engineering, like how to construct Prompts and make the most of what ChatGPT offers.

Key Highlights:

• Introduction to Generative-AI and ChatGPT

• Learn best practices for effective prompting

• Hands-on experience with prompting via ChatGPT API

• Learn to harness ChatGPT to summarize, expand, infer, and transform information

• Learn effective prompting for HR, SEO, Marketing, Professional writing, and academia

• Understanding of AutoPrompt, Chatbots, and ChatGPT’s limitations

Best for: Developers looking to leverage ChatGPT in projects

Here is the link to join this course — Master ChatGPT Prompt Engineering

3. Generative AI with Python and TensorFlow 2

If you’re comfortable with Python and want to dive deeper into generative AI, this course is an excellent choice.

It focuses on using Python libraries like TensorFlow and PyTorch to build generative models.

You’ll learn to create image generators, text generators, and even music generation systems.

Key Highlights:

• Hands-on projects in image, text, and audio generation

• Practical coding exercises using TensorFlow and PyTorch

• Advanced topics like GANs and transformer models

Best for: Python developers exploring AI applications

Here is the link to join this course — Generative AI with Python and TensorFlow 2

4. Mastering OpenAI API and ChatGPT for Innovative Applications

GPT-4 is one of the most powerful language models available today. This course focuses on how to use GPT-4 effectively, from content generation to complex data processing tasks.

Whether you’re a content creator, data scientist, or developer, this course helps you master GPT-4’s capabilities.

Key Highlights:

• Advanced features of GPT-4 for content generation

• How to use GPT-4 for code generation and debugging

• Integrating GPT-4 with existing workflows and applications

Best for: Developers interested in GPT-4-specific applications

Here is the link to join this course — Mastering OpenAI API and ChatGPT for Innovative Applications

5. Generative AI Handbook

This course is designed for business professionals and AI enthusiasts who want to understand where generative AI is headed. It covers emerging trends, ethical considerations, and practical applications in various industries.

Key Highlights:

• Understanding ethical concerns and challenges in generative AI

• Insights into emerging technologies and tools

• Case studies of real-world applications in industries like healthcare and gaming

Best for: Professionals looking to understand generative AI trends

Here is the link to join this course — Generative AI Handbook

Why Choose Educative.io?

Educative.io’s courses are ideal for learners of all levels. The platform offers a text-based, interactive learning experience that allows you to practice coding directly within the browser.

With no need for additional software setups, you can focus entirely on learning.

Their courses are designed by industry professionals, ensuring you get high-quality, relevant content that aligns with current industry standards.

Another great thing is that they are offering a big discount now, so if you want to level up your learning, now is the time to join.

Here is the link to join Educative — 50% Discount on Educative Unlimited

Final Thoughts

Generative AI and ChatGPT are opening up endless possibilities, and learning these technologies is no longer optional if you want to stay competitive.

The courses listed above are some of the best options on Educative.io for building expertise in these areas.

Take the first step toward mastering generative AI by enrolling in one of these courses today.

As the demand for AI expertise continues to grow, you’ll be positioning yourself for success in one of the most exciting fields of the future.

P. S. — If you are serious about Agentic AI and looking for a resource to master AI agents and Agentic AI, I also recommend you check out Agentic AI Engineering by Paul Iustzin, author of the bestselling book LLM Engineer’s Handbook.

Hello guys, Preparing for a tech interview can be daunting, but having the right resources can make a significant difference.

In the past, I have shared 11 system design interview resources and Java interview books, and today, I am going to add a few more gems to that list.

This is courtesy of Alex Xu of ByteByteGo, with some enhancements and additions from my side.

By the way, if you are looking for a one-stop shop to prepare for a coding interview, then DesignGuru.io is a great place to start.

They have many Grokking courses to prepare for coding interviews, like OOP Design, System Design, Dynamic Programming etc and you can get access to all of their courses for a big discount by joining their All course bundle. You can also use code GURU to get 30% discount.

6 Best Resources for Tech interviews (SDE) in 2026

Here are some highly recommended tools and materials to help you succeed in various aspects of your tech interviews.

1. Coding Interview Preparation

• AlgoMonster: Learn coding interview patterns that can be used to solve a variety of coding problems on LeetCode and NeetCode

• Educative-99: a curated set of 99 coding interview questions designed to help candidates master 26 essential problem-solving patterns. It provides a hands-on, setup-free coding environment where users can efficiently practice and internalize coding patterns crucial for tech interviews, making it easier to tackle various coding challenges in a structured manner​

• LeetCode: Practice coding problems of varying difficulty levels.

• NeetCode: Get access to a structured plan for mastering coding problems.

• Cracking the Coding Interview: A comprehensive guidebook with 189 programming questions and solutions.

2. System Design Round

• System Design Interview Books: Essential reads for understanding system design concepts and interview questions.

• Grokking the System Design Interview by Design Guru: A practical guide to system design with real-world scenarios.

• Codemia.io — Practice System Design Problems in Leetcode style on Codemia. They have more than 120 problems on System Design with editorial solutions and diagrams.

• Designing Data-Intensive Applications: Learn about the architecture of data systems and how to design data-heavy applications.

3. Behavioral Interview

• Tech Interview Handbook: A go-to resource for behavioral interview questions and tips.

• A Life Engineered (YouTube): Insights and advice from industry professionals on YouTube.

• STAR Method: A technique for answering behavioral questions by discussing the Situation, Task, Action, and Result.

4. Object-Oriented Design (OOD) Interview

• Interview-ready: Tools and courses to help you prepare for OOD interviews.

• Educative: Interactive learning paths for mastering design patterns and OOD principles.

• Codemia.io — They have recently added OOAD sections with many questions like parking lot design, vending machine design and much more.

• Head First Design Patterns Book: An engaging book that simplifies complex design patterns with practical examples.

5. Mock Interviews

• DesignGurus.io: if you want to do mock interviews with ex-Facebook and Microsoft engineers on System design and coding interviews, then DesignGurus.io is a great place.

• Interviewing.io: Practice interviews with experienced engineers.

• Pramp: Pair up with peers to practice coding and system design interviews.

• Meetapro: Book sessions with experts for personalized feedback and improvement.

You can also find mock interview videos on YouTube, like this, to get a feel before you actually book your mock interview session with these sites

6. Applying for Jobs

• LinkedIn: Utilize the professional network to find and apply for tech jobs.

• Monster: A comprehensive job search platform with numerous listings.

• Indeed: Search for tech roles across various companies and locations.

That’s all about the best resources for coding and tech interviews in 2026. Cracking a coding or tech interview requires a lot of preparation and hard work, and while it’s definitely not easy to find a new job, especially your first job, with these resources at your disposal, you’ll be well-prepared to tackle any tech interview.

All the best with your next interview!!

All the best for your System Design Interviews. If you have any doubts or questions, feel free to ask in the comments.

P. S. — If you just want to do one thing at this moment, go join ByteByteGo and start learning System Design concepts. You will thank me later. Make it a goal for the new year, and you will surely be a better Software Engineer.

Hello guys, if you are learning LLM and AI in 2026, then you should know about LangChain and LangGraph, two of the most popular LLM frameworks, and if you are looking for resources to learn them, then you have come to the right place.

The rise of Large Language Models (LLMs) like GPT-4, Gemini, and other AI technologies has transformed how we approach software development, data processing, and automation.

Tools like LangChain and LangGraph are at the forefront of this transformation, empowering developers to build powerful, production-ready AI applications with ease.

Now, we’ll explore the 7 best Udemy courses for mastering LangChain and LangGraph in 2026, and help you understand why these tools, along with Udemy’s courses, are your gateway to cutting-edge AI skills.

Though if you are in a hurry and just need one course to start with, then you can join the LangChain— Develop LLM Powered Applications with LangChain course on Udemy; it’s one of the most hands-on, detailed, and up-to-date courses. I highly recommend this to anyone who wants to master LangChain in 2026

Why Learn LangChain and LangGraph in 2026?

Before we dive into these online Udemy courses, it’s important to understand why LangChain and LangGraph are essential for AI development in 2026

LangChain

LangChain allows developers to quickly integrate Large Language Models (LLMs) like GPT into applications, enabling features such as natural language understanding, text generation, and AI-powered automation.

It simplifies the complex processes of model integration and accelerates AI app development, making it a go-to framework for creating LLM-powered applications.

LangGraph

LangGraph builds on LangChain’s capabilities and introduces the concept of AI agents. These agents can autonomously carry out tasks, making decisions based on user input and real-time data.

This makes LangGraph essential for developing more interactive, autonomous systems that can manage workflows or even communicate with other systems.

Both of these frameworks provide the tools to harness the power of LLMs, creating scalable, efficient, and innovative AI-driven applications, and that’s why I highly recommend that any AI engineer learn these frameworks

7 Best Udemy Courses to Learn LangChain and LangGraph in 2026

Without any further ado there are the best online courses you can join on Udemy to learn about LangChain and LangGraph in 2026

1. LangChain— Develop LLM-Powered Applications with LangChain

• Students: 71,009

• Why Take This Course: This best-selling Udemy course is ideal for anyone looking to get a deep dive into LangChain.

• In this LangChain course, you’ll learn how to build a real-world generative AI application powered by LLMs using the latest version of LangChain.

• Whether you’re a beginner or an intermediate AI enthusiast, this course walks you through practical applications of LangChain in Python.

• What You’ll Learn: Master LangChain, build LLM-powered applications, and get hands-on with Python integration.

Here is the link to join this course— LangChain — Develop LLM Powered Applications with LangChain

2. ChatGPT and LangChain: The Complete Developer’s Masterclass

Students: 20,220

Why Take This Course: This masterclass focuses on integrating ChatGPT with LangChain, enabling you to build production-ready AI applications. It’s an intensive course that not only covers the fundamentals but also helps you implement advanced real-world applications.

What You’ll Learn: ChatGPT integration, LangChain fundamentals, Python-based AI app development.

Here is the link to join this course — ChatGPT and LangChain: The Complete Developer’s Masterclass

3. LangChain Mastery: Develop LLM Apps with LangChain & Pinecone

Students: 17,961

Why Take This Course: This course combines the power of LangChain with Pinecone, a vector database, to help you develop sophisticated LLM apps. It’s a step-by-step guide on how to create production-ready AI apps using Python, Pinecone, OpenAI, and Gemini.

What You’ll Learn: LLM app development, LangChain integration with Pinecone, and building scalable AI systems.

Here is the link to join this course— LangChain Mastery: Develop LLM Apps with LangChain & Pinecone

4. LangChain with Python Bootcamp

Students: 14,169

Why Take This Course: This bootcamp focuses on the practical aspects of building real-world applications using LangChain. It’s designed for developers who want to gain hands-on experience with LLMs and Python.

What You’ll Learn: LangChain development, Python programming, and real-world applications.

Here is the link to join this course — LangChain with Python Bootcamp

5. Complete Generative AI Course With Langchain and Huggingface

Students: 27,801 students

Why Take This Course: If you’re just starting your journey with LangChain, this beginner-friendly course offers a step-by-step guide to building generative AI applications. You’ll master the fundamentals and build confidence as you develop your own LLM apps.

What You’ll Learn: LangChain basics, building generative AI apps, and Python programming for beginners.

Here is the link to join this course — Complete Generative AI Course With Langchain and Huggingface

6. LangGraph Mastery: Develop LLM Agents with LangGraph

Students: 690

Why Take This Course: This bestseller udemy course is designed to help you build AI agents using LangGraph and LangChain. You’ll learn how to create autonomous, intelligent systems capable of handling complex workflows.

What You’ll Learn: LangGraph, AI agents, Python, and advanced LLM concepts.

Here is the link to join this course — LangGraph Mastery: Develop LLM Agents with LangGraph

7. LangGraph - Develop LLM-Powered AI Agents with LangGraph

Students: 4,592

Why Take This Course: This course dives deep into LangGraph, teaching you how to build fast, real-world AI agents powered by LLMs. It’s perfect for those who want to specialize in developing intelligent AI-driven applications.

What You’ll Learn: AI agents, LangGraph, Python, and real-world applications.

Here is the link to join this course — LangGraph — Develop LLM Powered AI Agents with LangGraph

Why Udemy Courses Are Ideal for Learning LangChain and LangGraph?

You must be wondering why Udemy courses? Why not Coursera, Pluralsight, or just YouTube?

While all of these are great places to learn tech skills, Udemy offers a wide range of benefits for learners looking to specialize in AI and LLM-based frameworks like LangChain and LangGraph.

Here are 5 main reasons I think you should join Udemy to learn LLM frameworks like LangChain and LangGraph in 2026:

• Expert-Led Courses: Udemy’s instructors are often professionals with real-world experience in AI and software engineering, giving you direct access to industry insights and best practices.

• Practical Projects: Most of the courses on Udemy focus on hands-on learning, providing practical, real-world applications that help you build your skills as you go.

• Flexibility: With lifetime access to the course content, you can learn at your own pace and revisit topics anytime you want, which is perfect for learners who have busy schedules.

• Global Community: Learning through Udemy means you’ll be part of a larger global community, offering peer support, networking opportunities, and a space to ask questions and receive feedback.

Another important reason is price and affordability. Udemy is the only place on the internet where you can get a 50-hour, high-quality course for just $10 on SALE. You can’t get that kind of deal anywhere else.

Now, Udemy also has a Personal Plan, where you get access to the best of Udemy’s 11000+ courses for a monthly fee of $30. If you want to join multiple courses, then the Udemy Personal Plan is actually a better deal. You can also try it for free for 7 days to get a feel of it.

Conclusion

That’s all about the best Udemy courses to learn LangChain and LangGraph, two of the best LLM frameworks in 2026. Learning LangChain and LangGraph in 2026 is crucial if you’re interested in staying at the forefront of AI and LLM application development.

These Udemy courses provide a comprehensive learning path, from beginner to advanced levels, giving you the tools and techniques to build cutting-edge AI-powered solutions.

Whether you’re looking to build autonomous AI agents or LLM-powered applications, these courses offer hands-on projects, expert guidance, and the flexibility to learn at your own pace.

By choosing Udemy as your platform for learning, you’ll gain lifetime access to course materials, benefit from practical projects, and enjoy support from a global community of learners.

Now is the perfect time to jump into LangChain and LangGraph, mastering the tools that will shape the future of AI development.

P. S. — If you are serious about moving into the AI and Machine Learning field, I also suggest you learn Python, one of the most valuable skills when it comes to Machine Learning and AI. If you need resources, check out this list of the best Python courses to kick-start your journey.