Foundations
Machine Learning Fundamentals
Understand core ML concepts including supervised/unsupervised learning, training/validation/test splits, and evaluation metrics. Learn how to approach ML problems systematically.
Introduction
Understand core ML concepts including supervised/unsupervised learning, training/validation/test splits, and evaluation metrics. Learn how to approach ML problems systematically.
4 Lessons
25h Est. Time
4 Objectives
1 Assessment
By completing this module you will be able to:
✓ Understand supervised vs unsupervised learning paradigms
✓ Implement train-test split and cross-validation strategies
✓ Evaluate models using appropriate metrics
✓ Identify and mitigate overfitting and underfitting
Lessons
Work through each lesson in order. Each one builds on the concepts from the previous lesson.
1
Supervised Learning: Regression and Classification
2
Model Evaluation and Selection
3
Unsupervised Learning and Clustering
4
Feature Engineering and Data Pipelines
Recommended Reading
Supplement your learning with these selected chapters from the course library.
Machine Learning with PyTorch and Scikit-Learn
Chapters 1-4
Hands-on Machine Learning with Scikit-Learn and PyTorch
Chapters 1-2
Module Assessment
Machine Learning Fundamentals
Question 1 of 3