Classical Machine Learning
Build the statistical foundations underneath every generative-AI system: how models learn from labeled data, how to measure them honestly, and where they go wrong.
Every generative-AI system sits on top of the same statistical foundations: labeled data, loss functions, and honest evaluation. These lessons build that foundation with the algorithms and pitfalls that show up in any applied ML role.
Supervised Learning Explained
Understand how a model learns a mapping from inputs to known outputs, and why labeled data is the foundation of most production ML.
7 min read →Classification vs. Regression
Learn how to tell the two core supervised-learning problem types apart and pick algorithms that match the shape of your output.
7 min read →Model Evaluation Metrics Explained
Understand accuracy, precision, recall, F1, and RMSE well enough to know when each one is lying to you.
7 min read →Overfitting, Underfitting, and Regularization
Diagnose why a model performs well in training and poorly in the real world, and learn the standard techniques to fix it.
7 min read →Feature Engineering Basics
Learn the practical techniques for turning raw data into inputs a model can actually learn from.
7 min read →