ML Foundations Study Series (9 parts)

ML Foundations Study Series (9 parts)

Classical machine learning foundations on the runway to neural networks and local LLMs

---

Prerequisites	AI Basics Series (optional), basic Python
Next series	LLM Core Study Series (6 parts)

All parts

1	ML Foundations (1/9) — Machine Learning & sklearn: The Coordinate System of Learning
2	ML Foundations (2/9) — Nearest Neighbors: The Simplicity and Pitfalls of k-NN
3	ML Foundations (3/9) — Linear Regression: From Least Squares to Gradient Descent
4	ML Foundations (4/9) — Logistic Regression & Classification: Making Probabilities
5	ML Foundations (5/9) — Regularization & Model Selection: Taming Overfitting
6	ML Foundations (6/9) — Neural Networks: From Perceptron to MLP
7	ML Foundations (7/9) — Deep Learning Training: Optimizers, Regularization, Initialization
8	ML Foundations (8/9) — Deep Learning Architectures: CNN, RNN, Attention
9	ML Foundations (9/9) — PyTorch vs TensorFlow, and the Road to Local LLMs

Recommended pace

Each part takes 25–40 minutes on average. One to three parts per week is the sweet spot for retention.