<h2>About this course</h2>
<p>This course takes you deep into the mathematics and implementation of <a href="/topics/llm">large language models</a> (LLMs) — the AI systems behind ChatGPT, Claude, and LLaMA. Rather than using LLMs as a black box, you'll build core components like attention mechanisms, <a href="/topics/transformers">transformers</a>, and complete models from scratch in <a href="/topics/pytorch">PyTorch</a>, guided by Umar Jamil, a respected AI educator known for rigorous, hands-on instruction.</p>
<p>If you want to move beyond <a href="/topics/prompt-engineering">prompt engineering</a> and actually understand how these models work under the hood, this course closes the gap between theory and code.</p>

<h2>What you'll learn</h2>
<ul>
<li>Build attention mechanisms from first principles and understand why they're the foundation of modern LLMs</li>
<li>Implement transformer architectures in PyTorch — the backbone of models like GPT and BERT</li>
<li>Train and fine-tune LLaMA and Mistral models on custom datasets</li>
<li>Work with embeddings, tokenization, and positional encoding to prepare data for LLMs</li>
<li>Debug and optimize model training — learning practical tricks for faster convergence and better results</li>
<li>Understand the mathematical reasoning behind backpropagation in the context of <a href="/topics/neural-networks">neural networks</a></li>
<li>Read and implement research papers that describe state-of-the-art LLM architectures</li>
</ul>

<h2>Who this is for</h2>
<p>You're ready for this course if you're comfortable with Python, understand basic neural networks, and are tired of treating LLMs as a mystery. You don't need to be a PhD — but you do need curiosity about how things actually work.</p>
<ul>
<li><strong>ML engineers and <a href="/for/researchers">researchers</a></strong> — Build production-ready skills in model architecture and training, positioning yourself for AI engineering roles at startups and tech companies</li>
<li><strong><a href="/for/data-scientists">Data scientists</a> exploring AI specialization</strong> — Transition from general data work into the high-growth field of large language models, where demand and salaries are rising fastest</li>
</ul>

<h2>Prerequisites</h2>
<p>Solid knowledge of Python, NumPy, and PyTorch basics. Comfortable with linear algebra and calculus (gradients, matrix multiplication). This is an advanced course — beginner-friendly it is not.</p>

<h2>Why this matters for Indian learners</h2>
<p>LLM expertise is one of the fastest-growing skill gaps in India's AI job market. Companies like Google AI, Microsoft Research India, and dozens of startups in Bangalore, Delhi, and Hyderabad are actively hiring ML engineers and researchers who understand transformer architectures and can build custom models. Salaries for LLM-focused roles start significantly higher than general ML positions.</p>
<p>India's AI sector is growing rapidly, but most talent has only surface-level knowledge of LLMs. By mastering the fundamentals in this course, you're building a rare, in-demand skillset that directly translates to better roles and higher pay — whether you aim for a research position, an engineering team, or your own AI venture.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes. This course is hosted on YouTube and is completely free — no hidden fees, no paid certificate upsell. You just need a YouTube account and a willingness to dive into code and math.</p>
<h3>How long will it take to complete?</h3>
<p>Expect around 15 hours of video content. However, you'll want to pause, code along, and re-watch dense sections — so plan for 30–40 hours total over 4–6 weeks if you're coding every topic from scratch. Going slower is fine; mastery matters more than speed.</p>
<h3>Will I get a certificate?</h3>
<p>No formal certificate is issued. This course is self-directed learning. What you'll earn instead is real knowledge: you'll have working code, a deep understanding of LLM internals, and projects to show employers.</p>

Build LLMs from scratch — deep code walkthroughs.

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Umar Jamil's deep dives — building LLaMA, Mistral, BERT, attention from scratch in PyTorch.

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