<h2>About this course</h2>
<p>UC Berkeley's legendary CS188 is one of the world's most respected introductions to artificial intelligence — used by thousands of <a href="/for/students">students</a> and self-learners globally. This course covers the foundational algorithms and concepts that power modern AI systems: search strategies, decision-making under uncertainty, <a href="/topics/reinforcement-learning">reinforcement learning</a>, probabilistic reasoning with Bayesian networks, and machine learning. You'll move beyond theory by building intelligent agents, most famously Pacman that learns to navigate complex game worlds.</p>

<h2>What you'll learn</h2>
<ul>
<li>Search algorithms (depth-first, breadth-first, A*) to solve real-world planning and pathfinding problems</li>
<li>Markov Decision Processes (MDPs) and value iteration to make optimal decisions under uncertainty</li>
<li>Reinforcement learning techniques so agents can learn from experience and improve over time</li>
<li>Bayesian networks and probabilistic inference for reasoning with incomplete information</li>
<li>Machine learning fundamentals including supervised learning, classification, and feature engineering</li>
<li>How to design and debug intelligent agents through hands-on programming projects</li>
</ul>

<h2>Who this is for</h2>
<p>This course fits you if you want to understand how AI systems actually work — not just use them. You'll need comfort with programming (Python) and basic math, but you don't need prior AI experience.</p>
<ul>
<li>Engineering students — build a portfolio project (Pacman) that impresses in campus placements and tech interviews</li>
<li>Career switchers — master the AI fundamentals that top tech companies expect from junior engineers</li>
</ul>

<h2>Prerequisites</h2>
<p>You should be comfortable writing code in Python or another programming language, and comfortable with basic discrete mathematics (sets, logic, probability). Prior AI or ML experience is not required.</p>

<h2>Why this matters for Indian learners</h2>
<p>AI engineer roles in India are among the fastest-growing in tech, with strong demand at companies like Google, Amazon, Microsoft, and Indian startups. Learning search and decision-making algorithms — the bedrock of AI — gives you an edge in interviews and on the job. Whether you're aiming for roles in autonomous systems, recommendation engines, or data science, this course teaches the algorithmic thinking that differentiates senior engineers from those who only know libraries.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes, completely free. UC Berkeley makes the full course, lecture videos, and project specifications available online at no cost. You access everything through their official website.</p>
<h3>How long will it take to complete?</h3>
<p>The course is designed as 45 hours of work. If you dedicate 3–5 hours per week, you could finish in 9–15 weeks. The projects (especially Pacman) take time but teach you far more than lectures alone.</p>
<h3>Will I get a certificate?</h3>
<p>This course does not offer a completion certificate. However, you can link to your project code on GitHub or show your Pacman agent as portfolio evidence — which often impresses employers more than a certificate.</p>

Berkeley

UC Berkeley's intro AI course — search, MDPs, RL, Bayes nets, ML. Famous Pacman projects.

Berkeley CS188 Introduction to AI

<h2>About this course</h2>
<p>This is Sergey Levine's legendary deep <a href="/topics/reinforcement-learning">reinforcement learning</a> course from UC Berkeley, one of the most respected programs in <a href="/topics/ai-research">AI research</a>. You'll learn how machines can learn to make decisions through interaction with their environment—from game-playing AI to robotics and real-world control systems. This course matters because deep RL is the foundation behind breakthroughs like AlphaGo, autonomous systems, and the decision-making layers in modern AI applications.</p>

<h2>What you'll learn</h2>
<ul>
  <li>Policy gradient methods and actor-critic algorithms for training <a href="/topics/neural-networks">neural networks</a> to make sequential decisions</li>
  <li>Q-learning and deep Q-networks (DQN) for value-based reinforcement learning</li>
  <li>Model-based reinforcement learning: how to learn and use environment dynamics for planning</li>
  <li>Exploration strategies and the exploration-exploitation tradeoff in learning systems</li>
  <li>Offline reinforcement learning: learning from static datasets without active interaction</li>
  <li>Practical implementation in code: building RL agents from scratch using <a href="/topics/tensorflow">TensorFlow</a> or <a href="/topics/pytorch">PyTorch</a></li>
  <li>Real-world applications: robotics, game AI, autonomous systems, and recommendation engines</li>
</ul>

<h2>Who this is for</h2>
<p>You're a strong programmer and mathematician who wants to move beyond supervised learning. You're ready to understand how AI systems can learn through trial and error, and you have the patience for math-heavy content. This is an advanced course—not an introduction.</p>
<ul>
  <li><strong>Machine learning engineers</strong> — build production RL systems and understand the research driving autonomous systems and decision-making AI</li>
  <li><strong>AI <a href="/for/researchers">researchers</a></strong> — master the theory and algorithms shaping next-generation AI breakthroughs in robotics and control</li>
  <li><strong>Roboticists and control engineers</strong> — learn modern <a href="/topics/deep-learning">deep learning</a> approaches to robot learning and adaptive control</li>
</ul>

<h2>Prerequisites</h2>
<p>You'll need solid foundations in linear algebra, probability, and calculus. Programming fluency in Python is essential—you'll be implementing algorithms from scratch. Prior experience with supervised deep learning (CNNs, RNNs) or any machine learning course is strongly recommended. This is not a starting point for machine learning; it's a specialized advanced course.</p>

<h2>Why this matters for Indian learners</h2>
<p>India's AI and robotics sectors are growing fast. Companies like Flipkart, Amazon India, and emerging robotics startups are hiring deep RL specialists—roles that command premium salaries. Government initiatives in autonomous vehicles and smart cities create demand for RL engineers. Learning from Berkeley's top-tier curriculum gives you a global-standard skillset that Indian tech companies and international opportunities value highly.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes, completely free. UC Berkeley's RAIL lab publishes all lectures, slides, and assignments publicly. You can access everything without paying.</p>
<h3>How long will it take to complete?</h3>
<p>The course totals approximately 40 hours of content and assignments. For a typical learner, plan 8-10 weeks at a steady pace—roughly 4-5 hours per week. The math-heavy units may take longer; real-world RL projects may take shorter. Go at your own speed.</p>
<h3>Will I get a certificate?</h3>
<p>This course does not offer an official certificate of completion. However, you'll build a portfolio of RL projects (from lectures and assignments) that's far more valuable to employers than a certificate badge. Use your finished work as proof of mastery.</p>

Sergey Levine's deep RL course — policy gradients, Q-learning, model-based RL, exploration, offline RL.

Berkeley CS285 Deep Reinforcement Learning

<h2>About this course</h2>
<p>This Berkeley MOOC is a hands-on exploration of <a href="/topics/llm">large language models</a> (LLMs) in action—specifically, how to build agents that reason, use tools, and work together to solve problems. You'll learn directly from <a href="/for/researchers">researchers</a> at one of the world's leading AI institutions, with guest lectures from top labs bringing real-world context. Whether you're curious about how ChatGPT-like systems actually work behind the scenes or you want to build smarter AI applications, this course bridges theory and practice.</p>

<h2>What you'll learn</h2>
<ul>
<li>How LLM agents reason through complex tasks and break them into steps</li>
<li>Tool use and function calling—enabling LLMs to interact with APIs, databases, and real-world systems</li>
<li>Multi-agent systems—designing multiple agents that collaborate, debate, or specialize in different domains</li>
<li>Evaluation frameworks—measuring agent performance and reliability in production settings</li>
<li><a href="/topics/prompt-engineering">Prompt engineering</a> techniques that move beyond simple templates to structured reasoning</li>
<li>Building and debugging agentic workflows in Python</li>
<li>Real-world case studies from top research labs showing deployed agent systems</li>
</ul>

<h2>Who this is for</h2>
<p>You'll get the most from this course if you have some coding experience and basic familiarity with how LLMs work. You don't need a PhD—just curiosity and willingness to tinker with code.</p>
<ul>
<li>AI/ML enthusiasts — deepen your understanding of cutting-edge agent architectures beyond tutorials and blog posts</li>
<li>Software engineers — learn how to architect and deploy intelligent systems that extend beyond simple API calls</li>
</ul>

<h2>Prerequisites</h2>
<p>Basic Python programming experience and familiarity with how large language models (like ChatGPT or Claude) function. If you've worked with APIs or built simple Python projects, you're ready.</p>

<h2>Why this matters for Indian learners</h2>
<p>AI agent skills are becoming central to India's software export story. Companies like TCS, Infosys, and Wipro are building AI-driven solutions for global clients, and smaller startups in Bangalore, Hyderabad, and Pune are competing in the AI tools space. Understanding LLM agents—how to build them, evaluate them, and deploy them reliably—puts you ahead in job interviews and freelance work. This course gives you Berkeley-level knowledge without the fees or visa requirements.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes. You can access all course materials, lectures, and assignments at no cost. If you want a verified certificate of completion, Berkeley may offer that for a separate optional fee, but learning itself is completely free.</p>
<h3>How long will it take to complete?</h3>
<p>The course is designed for 20 hours of engaged learning. That's roughly 4–5 hours per week over a month, or you can go faster. Pace it to your schedule—there's no rigid deadline.</p>
<h3>Will I get a certificate?</h3>
<p>Yes. Upon completion, you'll receive a certificate from Berkeley acknowledging that you've finished the course. Many learners add this to LinkedIn profiles and portfolios.</p>

Berkeley's MOOC on LLM agents — reasoning, tool use, multi-agent systems, evaluation. Guest lectures from top labs.

Berkeley LLM Agents MOOC

<h2>About this course</h2>
<p>Full Stack <a href="/topics/deep-learning">Deep Learning</a>'s LLM Bootcamp is a free, hands-on introduction to building real applications with <a href="/topics/llm">large language models</a>. Created by Berkeley's renowned deep learning program, this course walks you through the entire lifecycle—from <a href="/topics/prompt-engineering">prompt engineering</a> and retrieval systems to <a href="/topics/fine-tuning">fine-tuning</a> models and deploying them with thoughtful user experience design. Whether you're curious about how ChatGPT-style tools work under the hood or want to build your own AI features, this bootcamp bridges the gap between "I've heard of LLMs" and "I can ship them."</p>

<h2>What you'll learn</h2>
<ul>
  <li>Master prompt engineering techniques to coax better responses from language models without retraining them</li>
  <li>Build retrieval-augmented generation (<a href="/topics/rag">RAG</a>) systems that let LLMs learn from your own documents and data</li>
  <li>Design autonomous <a href="/topics/ai-agents">AI agents</a> that can plan, reason, and take actions in multi-step workflows</li>
  <li>Fine-tune open-source language models on your own data to specialize them for specific tasks</li>
  <li>Deploy LLM applications to production with practical considerations for cost, speed, and reliability</li>
  <li>Think through user experience design when building AI-powered products—how to show uncertainty, handle errors, and build trust</li>
  <li>Understand the practical tradeoffs between different models, APIs, and deployment strategies</li>
</ul>

<h2>Who this is for</h2>
<p>You'll get the most from this course if you've written code before and want to level up into AI. You don't need a PhD in machine learning—just curiosity and comfort with Python.</p>
<ul>
  <li>Software engineers — add AI capabilities to your projects and stay relevant as LLMs reshape product development</li>
  <li>Builders and entrepreneurs — learn to prototype and launch AI-powered products without waiting for a machine learning team</li>
</ul>

<h2>Prerequisites</h2>
<p>Comfort with Python programming and basic familiarity with APIs and web development. No machine learning background needed.</p>

<h2>Why this matters for Indian learners</h2>
<p>India's tech sector is hungry for engineers who can build with LLMs. Companies across Bangalore, Delhi, and Mumbai—from startups to giants like Infosys and TCS—are expanding AI teams, and LLM skills command premium salaries. This bootcamp gives you exactly what Indian hiring <a href="/for/managers">managers</a> are looking for: not theory, but practical ability to ship AI features. As India's startup ecosystem grows and enterprises digitize, knowing how to build with LLMs positions you for roles in AI product teams, consulting, or your own ventures.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes, completely free. No hidden fees, no upsell to a paid certificate—just quality content from Berkeley's team available to anyone.</p>
<h3>How long will it take to complete?</h3>
<p>Expect about 15 hours of work total. That's roughly 2–3 hours per week if you spread it over a month, or you can move faster if you're in a hurry. Pace depends on how deep you go into the projects.</p>
<h3>Will I get a certificate?</h3>
<p>This course doesn't offer a completion certificate, but completing the projects and building something real for your portfolio will speak louder than a badge anyway.</p>

Full Stack Deep Learning's free LLM bootcamp — prompt eng, retrieval, agents, fine-tuning, deployment, UX.

Full Stack LLM Bootcamp

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