8 docs tagged with "overview"

Complete overview of the ML Foundations module - 12 lessons covering the core concepts every ML engineer must know before building production systems.

Master linear models from first principles - the mathematical foundation underlying deep learning, neural networks, and modern ML systems.

Master decision trees and ensemble methods from first principles - the model family that dominates tabular ML competitions and powers production fraud, pricing, and ranking systems worldwide.

A comprehensive engineering-focused guide to neural networks - from the perceptron to training dynamics, optimization, and production debugging.

How Shannon's information theory underpins every loss function, compression algorithm, and generative model in modern ML engineering.

Learn unsupervised learning algorithms - clustering, dimensionality reduction, and generative models - as applied in production ML systems.

A comprehensive module covering RL fundamentals through modern alignment techniques including RLHF and DPO, connecting classical theory to LLM training.

How probability theory underpins every machine learning algorithm - from loss functions to generative models to uncertainty quantification.