Machine Learning Foundations

Machine Learning Foundations

Machine Learning Foundations covers supervised and unsupervised learning, validation strategies, and practical model improvement approaches through applied projects.

What you'll learn, module by module

Updated June 2026

1. 1

   Module 1: ML Foundations - supervised vs unsupervised learning, the 2026 ML toolchain

2. 2

   Module 2: Feature Engineering with Pandas and NumPy

3. 3

   Module 3: Core Algorithms - linear/logistic regression, decision trees, random forests, gradient boosting (XGBoost)

4. 4

   Module 4: Model Validation - cross-validation, train/test splits, avoiding overfitting

5. 5

   Module 5: Error Analysis and Hyperparameter Tuning with Scikit-learn

6. 6

   Module 6: Unsupervised Learning - clustering and dimensionality reduction

7. 7

   Module 7: Model Deployment Basics - packaging a model for production use

8. 8

   Module 8: Capstone - fraud detection or demand forecasting project with full ML pipeline