learn-ai-engineering

Name: ashishps1/learn-ai-engineering
Author: ashishps1

Learn AI and LLMs from scratch using free resources

Subagents5.7k stars1.4k forksGPL-3.0Updated 4mo ago

Editor's note

This repository is a curated, link-only reference guide that organizes free and low-cost learning materials across the full AI engineering stack, from mathematical foundations through production agent systems. It covers discrete topic areas including linear algebra via 3Blue1Brown, machine learning via Google's Crash Course and Andrew Ng's Coursera specializations, deep learning frameworks such as PyTorch and TensorFlow, and LLM-specific subjects like transformer architecture, fine-tuning, reasoning models, and mixture-of-experts. Claude appears in two places: claude.ai is listed as an LLM chatbot alongside ChatGPT and Gemini, and Claude Code is listed under agentic coding tools alongside OpenAI Codex. The Anthropic API is also listed as an LLM API resource. The collection extends into prompt engineering, RAG, and MCP, reflecting its agentic-AI topic tags. The primary audience is self-directed learners, career changers, and developers building foundational AI knowledge without structured enrollment, as nearly every linked resource is free or freely auditable.

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Last scanned: 6/11/2026

Install as a Claude Code subagent

Method: Clone

Terminal

git clone https://github.com/ashishps1/learn-ai-engineering && cp learn-ai-engineering/*.md ~/.claude/agents/

1. Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).

2. Start a new Claude Code session to load the agents.

3. Delegate work to them with the Task/Agent tool or by name.

Use cases

AI / ML Education Productivity

About

Subagents overview

# Learn AI Engineering

A comprehensive collection of free resources to learn everything about AI/ML, LLMs and Agents.

## Mathematical Foundations
- [Mathematics Roadmap for Machine Learning](https://thepalindrome.org/p/the-roadmap-of-mathematics-for-machine-learning)
- [Essence of Linear Algebra - 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
- [Probability & Statistics - Khan Academy](https://www.khanacademy.org/math/statistics-probability)
- [Statistics Fundamentals - Josh Strarmer](https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9)
- [Mathematics for Machine Learning Specialization - Coursera (Andrew Ng)](https://www.coursera.org/specializations/mathematics-machine-learning)

## Python
- [AI Python for Beginners - Deeplearning.ai](https://www.deeplearning.ai/short-courses/ai-python-for-beginners/)

## AI & ML Fundamentals
- [Machine Learning Crash Course - Google](https://developers.google.com/machine-learning/crash-course)
- [AI for Beginners – Microsoft](https://microsoft.github.io/AI-For-Beginners/)
- [Elements of AI – University of Helsinki](https://course.elementsofai.com/)
- [Machine Learning Playlist - Josh Strarmer](https://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF)
- [Machine Learning Specialization - Coursera](https://www.coursera.org/specializations/machine-learning-introduction)

### Machine Learning Frameworks
- [Scikit-learn](https://scikit-learn.org/stable/)
- [XGBoost](https://xgboost.ai/)
- [LightGBM](https://lightgbm.readthedocs.io/en/stable/)
- [CatBoost](https://catboost.ai/)

## Deep Learning
- [Deep Learning Specialization - Coursera (Andrew Ng)](https://www.coursera.org/specializations/deep-learning)
- [Practical Deep Learning for Coders - Fast.ai](https://course.fast.ai/)
- [Mathematics for Deep Learning](https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/)
- [Deep Learning Playlist - Josh Starmer](https://www.youtube.com/playlist?list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1)

### Deep Learning Frameworks
- [TensorFlow](https://www.tensorflow.org/)
- [PyTorch](https://pytorch.org/)
- [Keras](https://keras.io/)

## Deep Learning Specializations
### Computer Vision
- [Deep Learning for Computer Vision - Stanford](https://cs231n.stanford.edu/)
### Natural Language Processing (NLP)
- [NLP Specialization - Coursera](https://www.coursera.org/specializations/natural-language-processing)
### Reinforcement Learning
- [Deep RL Course - Hugging Face](https://huggingface.co/learn/deep-rl-course/unit0/introduction)
- [Deep RL Bootcamp - UC Berkeley](https://sites.google.com/view/deep-rl-bootcamp/lectures)

## Generative AI
- [The Building Blocks of Generative AI](https://shriftman.substack.com/p/the-building-blocks-of-generative)
- [Generative AI for Beginners - Microsoft](https://github.com/microsoft/generative-ai-for-beginners)
- [Generative AI for Everyone - Coursera](https://www.coursera.org/learn/generative-ai-for-everyone)

## Large Language Models (LLMs)
- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)
- [Large Language Models explained briefly](https://www.youtube.com/watch?v=LPZh9BOjkQs)
- [Intro to LLMs](https://www.youtube.com/watch?v=zjkBMFhNj_g&pp=ygUDbGxt)
- [Understanding Large Language Models](https://magazine.sebastianraschka.com/p/understanding-large-language-models)
- [A Visual Guide to Reasoning LLMs](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-reasoning-llms)
- [Understanding Reasoning LLMs](https://magazine.sebastianraschka.com/p/understanding-reasoning-llms)
- [Understanding Multimodal LLMs](https://magazine.sebastianraschka.com/p/understanding-multimodal-llms)
- [A Visual Guide to Mixture of Experts (MoE)](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mixture-of-experts)
- [Finetuning Large Language Models](https://magazine.sebastianraschka.com/p/finetuning-large-language-models)
- [How Transformer LLMs Work](https://www.deeplearning.ai/short-courses/how-transformer-llms-work/)
- [Building GPT from scratch - Andrej Karpathy](https://www.youtube.com/watch?v=kCc8FmEb1nY)
- [LLM Course - GitHub](https://github.com/mlabonne/llm-course)
- [LLM Course - Hugging Face](https://huggingface.co/learn/llm-course/chapter1/1)
- [Awesome LLM Apps - GitHub](https://github.com/Shubhamsaboo/awesome-llm-apps)

### LLM Chatbots
- [ChatGPT](https://chatgpt.com/)
- [Gemini](https://gemini.google.com/app)
- [Claude](https://claude.ai/new)
- [Perplexity](https://www.perplexity.ai/)

### Open Source LLMs
- [Llama](https://www.llama.com/)
- [Deepseek](https://chat.deepseek.com/)

### LLM APIs
- [OpenAI](https://platform.openai.com/docs/overview)
- [Anthropic](https://docs.anthropic.com/en/docs/overview)
- [Gemini - Google](https://ai.google.dev/gemini-api/docs)
- [Groq - Inference](https://groq.com/)

### LLM Tools & Frameworks
- [LangChain](https://www.langchain.com/)
- [LlamaIndex](https://www.llamaindex.ai/)
- [Ollama](https://ollama.com/)
- [Instructor](https://python.useinstructor.com/)
- [Outlines](https://github.com/dottxt-ai/outlines)

### LLM Based IDEs
- [Cursor](https://www.cursor.com/)
- [Windsurf](https://windsurf.com/editor)
- [GitHub Copilot](https://github.com/features/copilot)

### Agentic Coding Tools
- [Claude Code](https://code.claude.com/docs/en/overview)
- [Codex](https://openai.com/codex/)

## Prompt Engineering
- [Google Prompting Essentials](https://www.coursera.org/google-learn/prompting-essentials)
- [ChatGPT Prompt Engineering for Developers - Deeplearning.ai](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/)
- [Advanced Prompting Techniques - Instructor](https://python.useinstructor.com/prompting/)
- [Prompt Engineering Techniques - Github](https://github.com/NirDiamant/Prompt_Engineering)
- [Getting Structured LLM Output - Deeplearning.ai](https://www.deeplearning.ai/short-courses/getting-structured-llm-output/)
- [God Tier Prompts](https://www.godtierprompts.com/)

## Retrieval-Augmented Generation (RAG)
- [Introduction to RAG - Coursera](https://www.coursera.org/projects/introduction-to-rag)
- [RAG Techniques - Github](https://github.com/NirDiamant/RAG_Techniques)

## AI Agents
- [A Visual Guide to LLM Agents](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents)
- [Agents - Chip Huyen](https://huyenchip.com/2025/01/07/agents.html)
- [AI Agents Course - Hugging Face](https://huggingface.co/learn/agents-course/)
- [Building AI Browser Agents - Deeplearning.ai](https://www.deeplearning.ai/short-courses/building-ai-browser-agents/)
- [GenAI Agents - Github](https://github.com/NirDiamant/GenAI_Agents)
- [AI Agents in Action, Second Edition - Book](https://www.manning.com/books/ai-agents-in-action-second-edition)

## Model Context Protocol (MCP)
- [MCP - Anthropic Guide](https://modelcontextprotocol.io/introduction)
- [Building AI Apps using MCP](https://www.deeplearning.ai/short-courses/mcp-build-rich-context-ai-apps-with-anthropic/)
- [MCP Course - Hugging Face](https://huggingface.co/learn/mcp-course/unit0/introduction)
- [Awesome MCP Servers - Github](https://github.com/punkpeye/awesome-mcp-servers)

## MLOps & Deployment
- [ML in Production - Coursera](https://www.coursera.org/learn/introduction-to-machine-learning-in-production)
- [Full Stack Deep Learning](https://fullstackdeeplearning.com/course/2022/)
- [ML System Design - Stanford](https://stanford-cs329s.github.io/syllabus.html)

### Tools
- [Streamlit](https://streamlit.io/)
- [MLflow](https://mlflow.org/docs/latest/index.html)

## Guides
- [OpenAI Cookbook](https://cookbook.openai.com/)
- [Anthropic courses](https://github.com/anthropics/courses/tree/master)

## Books
- [Hands-On Machine Learning](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)
- [Deep Learning - Ian Goodfellow](https://www.deeplearningbook.org/)
- [Deep Learning with Python](https://www.amazon.in/Deep-Learning-Python-Francois-Chollet/dp/1617294438/)
- [Why Machines Learn](https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749)
- [Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)
- [AI Engineering](https://www.oreilly.com/library/view/ai-engineering/9781098166298/)
- [Build a LLM from Scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
- [Prompt Engineering for LLMs](https://www.oreilly.com/library/view/prompt-engineering-for/9781098156145/)
- [Natural Language Processing with Transformers](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/)
- [Build a Multi-Agent System (from Scratch)](https://www.manning.com/books/build-a-multi-agent-system-from-scratch)
- [Build a Reasoning Model (From Scratch)](https://www.manning.com/books/build-a-reasoning-model-from-scratch)
- [Build an AI Agent (From Scratch)](https://www.manning.com/books/build-an-ai-agent-from-scratch)
- [Build an LLM Application (from Scratch)](https://www.manning.com/books/build-llm-applications-from-scratch)
- [AI Agents in Action](https://www.manning.com/books/gpt-agents-in-action)
- [AI Agents in Action, Second Edition](https://www.manning.com/books/ai-agents-in-action-second-edition)
- [LLMs in Production](https://www.manning.com/books/llms-in-production)

## YouTube Channels
- [Andrej Karpathy](https://www.youtube.com/@AndrejKarpathy)
- [3Blue1Brown](https://www.youtube.com/@3blue1brown)

## Other Resources
- [Papers with Code](https://paperswithcode.com/)
- [Kaggle Competitions](https://www.kaggle.com/competitions)

## Must-Read AI Papers
- [Attention Is All You Need](https://arxiv.org/pdf/1706.03762)
- [Generative Adversarial Networks (GANs)](https://arxiv.org/abs/1406.2661)
- [GPT: Improving Language Understanding by Generative Pre-Training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)
- [GPT-3: Language Models are Few-Shot Learners](

Topics

agentic-aiagentsaideep-learninggenerative-ailarge-language-modelsllmmachine-learningmcpmlprompt-engineeringrag

Frequently asked

What people ask about learn-ai-engineering

What is ashishps1/learn-ai-engineering?

ashishps1/learn-ai-engineering is subagents for the Claude AI ecosystem. Learn AI and LLMs from scratch using free resources It has 5.7k GitHub stars and was last updated 4mo ago.

How do I install learn-ai-engineering?

You can install learn-ai-engineering by cloning the repository (https://github.com/ashishps1/learn-ai-engineering) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.

Is ashishps1/learn-ai-engineering safe to use?

Our security agent has analyzed ashishps1/learn-ai-engineering and assigned a Trust Score of 100/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.

Who maintains ashishps1/learn-ai-engineering?

ashishps1/learn-ai-engineering is maintained by ashishps1. The last recorded GitHub activity is from 4mo ago, with 6 open issues.

Are there alternatives to learn-ai-engineering?

Yes. On ClaudeWave you can browse similar subagents at /categories/agents, sorted by popularity or recent activity.

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