AI & Machine Learning

From Mathematical Foundations to Agentic AI. Click tiers for resources, topics for notes.

Level 1 — Start Here 1

Mathematical Foundations

Linear algebra, calculus, probability, optimization

Linear Algebra Calculus Probability & Statistics Optimization Python for ML

Level 2 — Classical Learning 2

Machine Learning

Supervised, unsupervised, evaluation, ensembles

Supervised Learning Unsupervised Learning Model Evaluation Ensemble Methods Feature Engineering

Level 3 — Neural Networks 3

Deep Learning & RL

Neural networks, CNNs, RNNs, attention, reinforcement learning

Neural Networks CNNs RNNs & LSTMs Attention Reinforcement Learning Frameworks

Level 4 — Generative Power 4

Generative AI

Transformers, LLMs, RAG, fine-tuning, prompting

Transformers LLMs & Tokenization Vector Databases RAG Systems Fine-tuning & Alignment Prompt Engineering

Level 5 — The Summit 5

Agentic AI & Orchestration

Cognitive architectures, tools, memory, multi-agent systems

Cognitive Architectures Tool Use & Actions Agentic Memory Multi-Agent Systems Evaluation & Observability Safety & Guardrails