21 chapters, from "what is learning?" to shipping a model in production — plus ML Studio, a companion app where every dataset, model, and lab from the book runs live on your own device.
Every chapter starts in plain words before it shows an equation — and when the equations come, every symbol is explained. Nothing is assumed.
Every code example in the book is runnable — in the companion app on your phone, or in your own Python with NumPy and scikit-learn.
Six hands-on labs, ten worked examples, and a quiz for every chapter — with your progress tracked as you go, entirely on your own device.
A complete, self-contained course in classical machine learning. It starts with the question "what is learning?" and builds up through ensembles, kernels, and clustering to two full case studies — then hands off to neural networks, where the companion app ANN Studio continues the story.
| Ch. 1 | What is Machine Learning? |
| Ch. 2 | The ML workflow, end to end |
| Ch. 3 | Data, features & preprocessing |
| Ch. 4 | Evaluation & validation |
| Ch. 5 | Bias, variance & regularization |
| Ch. 6 | Linear regression |
| Ch. 7 | Logistic regression |
| Ch. 8 | k-Nearest Neighbors |
| Ch. 9 | Decision trees |
| Ch. 10 | Random forests and boosting |
| Ch. 11 | Neural networks in brief — the bridge |
| Ch. 12 | Clustering with k-means |
| Ch. 13 | Dimensionality reduction: PCA |
| Ch. 14 | Anomaly detection |
| Ch. 15 | Feature engineering & selection |
| Ch. 16 | Imbalanced data, thresholds & calibration |
| Ch. 17 | Hyperparameter tuning & model selection |
| Ch. 18 | Interpretability, fairness & ethics |
| Ch. 19 | From notebook to production |
| Ch. 20 | Case study: predicting house prices |
| Ch. 21 | Case study: customer churn |
| App. A | Mathematics when you need it |
| App. B | Python, NumPy & scikit-learn field guide |
Every dataset, model, and lab from the book, running live on your own device. No account, no internet connection required, nothing ever leaves your phone.
Pick a dataset and a model, then watch it fit in real time — decision boundaries, loss, and accuracy update live.
Six focused simulations: bias-variance, cross-validation, ROC & threshold, PCA, boosting, and SVM margins.
The full book, on your device — 21 chapters plus two appendices, organized to match the print edition exactly.
Worked examples computed live, and a quiz for every chapter, with your progress tracked across the whole course.
Installs are currently limited to approved testers. To get access, submit the email on your Android device's Google account below — once it's added to the tester list, you'll be able to install the app from the Play Store.