Intro to Machine Learning

Models, validation and first steps

This introduction covers the core ideas behind machine learning through a sequence that starts with how models work and moves into exploring data, building a first model and measuring its performance. Model validation is treated as a deliberate step, so you learn to compare alternatives rather than trusting a single score.

Concepts that carry over

• Underfitting and overfitting, and how to tune a model against them.
• Random forests, presented as a more sophisticated algorithm than a first model.
• Competitions as a way to keep improving once the basics are in place.

Who should start here

It is written for people new to machine learning who want to build their first models step by step. The sequence is deliberately gradual, so each lesson builds on the one before.