Machine Learning

Watch the algorithms learn.

Understand how models actually learn — regression, k-means, k-NN, decision trees and neural nets — with animated visualizers that show the math in motion.

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Chapter 1

ML Foundations

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01Beginner

What is Machine Learning?

Discover how machines learn patterns from data instead of following hand-written rules — and why that changes everything.

02Beginner

Types of Machine Learning

Discover the three core families of machine learning — supervised, unsupervised, and reinforcement learning — and learn which to reach for when.

03Beginner

Data, Features & Labels

Learn how machine learning sees the world: rows as examples, columns as features, and one special column called the label that the model tries to predict.

Chapter 2

Supervised Learning

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04Beginner

Linear Regression

Learn how machines draw the best-fit line through data to predict numbers — from house prices to tomorrow's temperature.

05Intermediate

Gradient Descent

Discover how machine learning models find their best settings by repeatedly nudging in the direction that shrinks their mistakes.

06Intermediate

Logistic Regression

Discover how logistic regression turns any number into a probability — and makes yes/no decisions with surprising elegance.

07Beginner

k-Nearest Neighbors

Classify anything by asking its neighbors — learn how k-NN works by computing distances and taking a majority vote, all from scratch.

08Intermediate

Decision Trees

Learn how machines make decisions by asking a series of yes/no questions — and why the right questions make all the difference.

Chapter 3

Unsupervised Learning

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09Intermediate

k-Means Clustering

Learn how k-Means groups unlabeled data into clusters by repeatedly assigning points to their nearest centroid and moving centroids to the center of their group.

Chapter 4

Model Quality

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10Intermediate

Overfitting & Underfitting

Discover why a model that aces its practice tests can still fail the real exam — and how to find the sweet spot that actually generalizes.

11Intermediate

Evaluating Models

Learn how to tell whether a machine learning model is actually good — and why a naive 99% accuracy score can be a complete lie.

Chapter 5

Neural Networks

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12Intermediate

Neural Networks: The Basics

Peek inside the artificial neuron — the tiny building block that, when stacked into layers, lets machines recognize faces, translate languages, and beat world champions at chess.

Chapter 6

Putting It Together

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13Beginner

ML in Practice

Walk through the real machine learning workflow — from messy data to a deployed model — and learn the essential tools, pitfalls, and next steps every practitioner needs.