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Summary here
Module · 5 lessons
Machine Learning Foundations
Learn the end-to-end ML workflow, your first algorithm (k-NN), and how to evaluate and tune models.
At a glance
Level
Beginner
Lessons
5 lessons
Time to complete
1–2 weeks
Cost
Free forever · no sign-up
This module introduces the core machine learning workflow that every project follows. You will train your first model with the k-Nearest Neighbors algorithm, learn how to measure whether a model is any good, and tune it to get the best performance — the foundation for everything that follows.
Lessons in this module
1
The Machine Learning Workflow
Learn the end-to-end machine learning workflow by building a real classifier with scikit-learn
2
Introduction to K-Nearest Neighbors
Learn how k-nearest neighbors classifies customers and build it on real bank marketing data
3
Evaluating Model Performance
Go beyond accuracy with confusion matrices, precision, recall, and the ROC curve on real bank data
4
Hyperparameter Optimization
Tune KNN settings with cross-validation and grid search to squeeze the best performance from a real classifier
5
Guided Project: Predicting Breast Cancer Diagnosis
Build an end-to-end k-NN classifier on the real Wisconsin breast cancer dataset
Achievement
Complete all 5 lessons to finish the Machine Learning Foundations module.