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Foundations

Phase 0: Course Setup
Phase 1: Python Fundamentals
- Phase 1: Python Fundamentals Catalog
Phase 2: Data Science Foundations
Phase 3: Mathematics for ML

Core AI

Phase 4: Tokenization
Phase 5: Embeddings
Phase 6: Neural Networks

Applied AI

Phase 7: Vector Databases
Phase 8: RAG
Phase 9: MLOps
Phase 10: Specializations

Advanced

Phase 11: Prompt Engineering
Phase 12: LLM Fine-Tuning
Phase 13: Multimodal AI
Phase 14: Local LLMs
Phase 15: AI Agents

Supplementary

Phase 16: Model Evaluation
Phase 17: Debugging & Troubleshooting
Phase 18: Low-Code AI Tools
Phase 19: AI Safety & Red Teaming
Phase 20: Real-Time & Streaming AI

Reference

Phase 21: Quizzes
Phase 22: References & Hands-On Labs
Phase 23: Glossary & Foundations
- Phase 23: Glossary & Foundations Catalog
- AI/ML Glossary

Resources

Master Study Guide
Career Roadmap
Interview Preparation
AI/ML Decision Matrices & Comparison Guides
Setup Guide - Zero to AI
AI/ML Learning Checklist ✅

Research

Phase 24: Advanced Deep Learning
Phase 25: Reinforcement Learning
Phase 26: Time Series Analysis & Forecasting
Phase 27: Causal Inference

Production

Phase 28: Practical Data Science
Phase 29: AI Hardware & Validation
Phase 30: Inference Optimization & Model Serving
- Phase 30: Inference Optimization & Model Serving Catalog
- vLLM Quickstart: High-Throughput Model Serving

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Stanford CS229 Machine Learning¶

Notebooks covering the Stanford CS229 ML theory and algorithms course.

CS229: Machine Learning Course (Stanford University)

1. Matrix Bernstein Inequality

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