machine-learning-course

Why we included this project

This is a notebook-driven machine learning course built around Python and scikit-learn, meant for teaching rather than deployment. It moves through the fundamentals in a sensible order, starting with linear regression, overfitting, regularization, and cross-validation, then covering supervised methods such as decision trees, k-nearest neighbors, naive Bayes, logistic regression, and SVMs, and finally unsupervised learning. Each topic pairs a written tutorial with a runnable Jupyter notebook, so you can read the explanation and run the code yourself to see how it behaves. That makes it a practical on-ramp for developers and data scientists who want to build intuition before reaching for a framework, and it also works as a quick reference when you need to revisit a specific algorithm. Since the material is organized by topic with code beside each explanation, teams can lift individual notebooks as starting points for their own experiments.