A curated list of truly awesome AI engineering learning resources, frameworks and libraries. Inspired by awesome-python.
- Introduction to AI
- Python programming
- Mathematics fundamentals
- Machine learning basics
- Neural networks and deep learning
- Large language models (llms)
- Prompt engineering
- Retrieval augmented generation
- AI agents
- MCP
- Evaluating AI systems
- Dataset engineering
- Deploying AI to production
- General-purpose AI frameworks
- LLM development
- Agent and workflow frameworks
- Evaluation frameworks
- Observability
- Dataset frameworks
- Core ML libraries
- NLP, LLM and transformer libraries
- Vector databases
- Data processing libraries
- Utility libraries
Paid alternatives
- Introduction to Computer Science and Programming - edX MITx
- Introduction to Computational Thinking and Data Science - edX MITx
- Essence of Linear Algebra – 3Blue1Brown
- Essence of Calculus – 3Blue1Brown
- Probability Explained – Khan Academy
- Statistics Fundamentals – Khan Academy
Paid alternatives:
Paid alternatives:
- Book: The Hundred-Page Machine Learning Book – Andriy Burkov
- Book: Hands-On Machine Learning with Scikit-Learn and PyTorch
- Video Course: Machine Learning Professionnal Certificate
- Practical Deep Learning Course
- Neural Networks and Deep Learning – Michael Nielsen
- Neural Networks – 3Blue1Brown
- Prompt Engineering Guide - Dair AI
- Prompt Engineering Guide - OpenAI
- Prompt Engineering Overview - Elvis Saravia
- Build a Retrieval Augmented Generation (RAG) App - LangChain
- Short-term memory - LangChain
- Long-term memory - LangChain
- Hugging Face Datasets
- Argilla
- Bespoke Curator
- Unsloth Dataset Tools
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