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PractitionerML101

Introduction to Machine Learning

A practical introduction to machine learning for working professionals. Build regression, classification and clustering models in scikit-learn, evaluate them honestly, tune them, and prepare them for deployment.

Lessons are AI-assisted and human-reviewed. Learn more.

Syllabus

Foundations

01
Fundamentals of Machine LearningFree preview
35 min

Supervised Learning

02
Regression Analysis: Predicting Continuous Values
35 min
03
Classification: Predicting Categories
40 min

Unsupervised Learning

04
Clustering: Discovering Patterns Without Labels
38 min
05
Dimensionality Reduction: Simplifying Without Losing Signal
35 min

Advanced Algorithms

06
Decision Trees and Ensemble Methods
42 min
07
Neural Networks and Deep Learning Basics
45 min

Model Validation

08
Model Evaluation and Metrics
40 min
09
Hyperparameter Tuning and Model Selection
40 min

Production and Scale

10
ML Workflow and Deployment
45 min