Machine Learning Course Series

Abstraction: Three-course ML curriculum from Python basics to LLMs

Key points:

• ML0 covers Python fundamentals for ML: data structures, functions, libraries; ML1 introduces core ML concepts including cross-validation, regression, tree methods, SVD, and dimensionality-lifting; ML2 advances to neural architectures, backpropagation, LLMs, and RAG
• ML2 topics include Word2Vec/GloVe embeddings, autoencoders, attention mechanisms, and transition from supervised learning to generative models
• Supplementary ML3 series offers deep dives: SVD, reinforcement learning (Q-learning to deep RL), PCA, Markov chains and HMMs, transfer learning, RAG, and Bayesian/MCMC methods
• Covers LLM API integration and LLM performance evaluation as dedicated modules
• Practical projects include neural network for California Housing Dataset price prediction

Connections: Machine Learning · Large Language Models · Neural Networks · Reinforcement Learning

Source: https://johnsanterre.github.io/ml_website/