amitshekhariitbhu/ai-engineering-course
AI Engineering Course - A free and complete AI Engineering Course to learn AI Engineering step by step - from Machine Learning, Neural Networks, and Transformers to LLMs, Fine-Tuning, RAG, AI Agents, LLM Inference, Evaluation, AI Safety, and AI System Design.
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README & documentation
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AI Engineering Course - A free and complete AI Engineering Course to learn AI Engineering step by step - from Machine Learning, Neural Networks, and Transformers to LLMs, Fine-Tuning, RAG, AI Agents, LLM Inference, Evaluation, AI Safety, and AI System Design. Every lesson comes with a detailed blog, and many lessons come with a video. This AI Engineering Course is a free, structured, and step-by-step curriculum to learn AI Engineering from scratch. It has 18 modules and 146+ in-depth lessons, and every lesson is a detailed blog that explains one concept in simple words with examples, diagrams, and math wherever it is needed. In simple words, this is the course that I wish I had when I started learning AI Engineering. We start with the basics of Machine Learning, and slowly move to how a Transformer works from the inside, how an LLM generates text, how we fine-tune and align…
Read the full README ↗ · Preview checked 2026-09-25T12:32:40.395Z
Inside the original README — Document outline
- AI Engineering Course
- Prepared and maintained by the Founder of Outcome School: Amit Shekhar
- Follow Amit Shekhar
- Follow Outcome School
- I teach at Outcome School
- Table of Contents
- About This AI Engineering Course
- What is AI Engineering?
- Who is This AI Engineering Course For?
- What Will We Learn in This AI Engineering Course?
- Prerequisites for This AI Engineering Course
- How to Use This AI Engineering Course
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Links from the README
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- Module 7: Training, Fine-Tuning, and Alignment
- Module 12: LLM Inference Engineering
- Module 13: Evaluation and Observability
- Module 14: AI Safety and Security
- AI Engineering Course FAQs
- License
- What is Deep RL from Human Preferences? The Paper That Started RLHF
- How to Chunk Documents for RAG? Chunking Strategies Explained
- LLM Inference Optimization
- Prefill vs Decode: LLM Inference Optimization
- What is Prefill-Decode Disaggregation in LLM Inference?
- LLM Inference Optimization
Documentation belongs to its respective authors. Reported project/model license: Apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
What this repository does
AI Engineering Course - A free and complete AI Engineering Course to learn AI Engineering step by step - from Machine Learning, Neural Networks, and Transformers to LLMs, Fine-Tuning, RAG, AI Agents, LLM Inference, Evaluation, AI Safety, and AI System Design.
Repository facts
- Owner
- amitshekhariitbhu
- Primary language
- Markdown
- Stars
- 293
- Forks
- 55
- Open issues + pull requests
- 0
- License
- Apache-2.0
- Archived
- No
- Default branch
- main
- Created
- 2026-09-23T08:04:42.000Z
- Last push
- 2026-09-23T08:26:56.000Z
Topics and intended use
Owner-supplied topics: ai-course, ai-engineering, ai-engineering-course, ai-engineering-curriculum, large-language-models
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Source: GitHub. Metadata observed 2026-09-25T12:03:43.116Z. Daily imports are snapshots, not real-time monitoring.
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