Machine Learning

Complete interactive reference covering 38 topics — from linear algebra fundamentals to diffusion models. Every topic has core math, visual intuition, and PyTorch code.

31 practical topics — model evaluation, feature analysis, backtesting, decision-making, and Python power tools. Built for practitioners who need answers, not theory.

30 topics from tokenization to agents — transformer internals, training paradigms (RLHF, DPO, LoRA), inference optimization, RAG, and evaluation benchmarks.

25 topics on deploying and operating ML systems at scale — model serving, monitoring, pipelines, feature stores, and governance. From experiment tracking to incident response.

25 topics on forecasting and temporal data — from stationarity and ARIMA to LSTMs, Transformers, and production backtesting. Interactive visualizations for every model.

6 hands-on activities — linear regression, K-Means, classification boundaries, neural networks, feature scaling, and timeseries forecasting. Build intuition by experimenting.