Machine Learning, Visualized
From linear regression to neural networks — learn ML with interactive examples and hands-on games.
Foundations
What Is Machine Learning?
Teaching computers to learn from examples instead of following rigid rules→
Types of Machine Learning
Three classrooms, three teaching styles — supervised, unsupervised, and reinforcement→
Features & Labels
Ingredients are features, the dish name is the label — teach your model what to look at and what to predict→
Train-Test Split
Practice with homework, get graded on new questions — why you must split your data→
Overfitting & Underfitting
Goldilocks and the three models — too simple, too complex, just right→
Core Algorithms
Linear Regression
Draw the best straight line through your data→
Logistic Regression
Yes or no? Draw the line that separates them→
K-Nearest Neighbours (KNN)
Ask your closest neighbours, go with the majority→
Decision Trees
A flowchart that learns which questions to ask→
Random Forests
Many trees vote together — wisdom of the crowd→
Neural Networks
Practical ML
Bias vs Variance
Too simple or too wobbly — find the sweet spot→
Cross-Validation
Test on every fold so no data goes to waste→
Confusion Matrix
True positives, false alarms — measure what matters→
Feature Engineering
Turn raw data into inputs a model can actually use→
Regularization
Penalise complexity so the model stays honest→
Unsupervised Learning
Model Deployment
Ethics & Fairness