Ch 9 · The ML Workflow · Machine Learning & AI

Topic 9 of 13 in Machine Learning & AI — Foundations — 1 lesson.

The End-to-End Pipeline

Every ML project, from a school exercise to a production system, follows the same loop: collect and clean data, split it, train, tune, evaluate, then deploy. It is a loop rather than a straight line because the world keeps changing, so models need monitoring and retraining. As you read the steps, notice that the test set is used only once, right at the end, which keeps the final score honest.

All topics in Machine Learning & AI Beginner

1. What Is Machine Learning?
2. Data — Features, Labels & Splitting
3. Linear Regression — Fitting a Line
4. How Models Learn — Gradient Descent
5. Classification — Predicting Categories
6. Decision Trees
7. Evaluating a Model Honestly
8. Overfitting & the Bias-Variance Tradeoff
9. The ML Workflow
10. Mini-Project — A Spam Classifier
11. Clustering — Finding Groups Without Labels
12. Your First Real scikit-learn Model
13. Words as Numbers — A Taste of NLP