Module 9 of 16 · Build: Code & Create
Teach Machines: Machine Learning
Train your own AI! Classification, regression, and real ML projects.
45 chapters
What this module covers
Teach Machines is the core machine learning module. It starts with the learning loop: a model makes a prediction, measures its error and adjusts. Students see why training data matters, and what good and bad examples do to a model.
They then work through supervised learning, classification and regression, and three classic algorithms: k-nearest neighbours, decision trees and random forests. The last chapters cover how to grade a model honestly: evaluation, the confusion matrix, overfitting and underfitting, cross-validation and feature engineering.
Start coding in Python! Build your first AI projects — chatbots, image editors, games, and more.
Chapter topics
- Machines That Learn
- Training Data Explained
- Good Examples, Bad Examples
- The Learning Loop
- Supervised Learning
- Classification Problems
- Regression Problems
- K-Nearest Neighbors
- Decision Trees
- Random Forests
- Model Evaluation
- Confusion Matrix
- Overfitting & Underfitting
- Cross Validation
- Feature Engineering
Plus 30 more chapters in the app.
Activities
- Train Your Model
- Accuracy Challenge
- Kaggle Mini-Competitions
After this module, students can
- Explain the difference between classification and regression
- Train and evaluate a simple model and read its confusion matrix
- Spot overfitting and explain how cross-validation helps
Practise it with these tools
Decision Tree BuilderDesign decision trees from scratch and watch them classify data in real time.
Data SorterLabel training data and see how labels shape what the model learns.
Confusion MatrixUnderstand true positives, false negatives, and how we grade AI models.
Pattern PlaygroundFeed in data and let the AI surface patterns the way a model would.
Go deeper
Start learning in the app
The first 3 chapters are free, no card needed.