Learn Machine Learning
Train models that learn from data. A free, structured curriculum: 5 units · 20 lessons · self-paced.
Curriculum outline
Unit 1: Getting Started with Machine Learning
Lesson 1.1: What Is Machine Learning and Why Learn It
Lesson 1.2: Setting Up Your Environment
Lesson 1.3: Your First Hands-On Exercise
Lesson 1.4: Core Vocabulary and Concepts
Unit 2: Beginner Fundamentals
Lesson 2.1: Essential Techniques: The Basics
Lesson 2.2: Common Patterns and Best Practices
Lesson 2.3: Working Through Simple Exercises
Lesson 2.4: Debugging and Fixing Mistakes
Unit 3: Intermediate Skills
Lesson 3.1: Leveling Up: Intermediate Techniques
Lesson 3.2: Working with More Complex Scenarios
Lesson 3.3: Combining Multiple Skills
Lesson 3.4: Real-World Intermediate Projects
Unit 4: Advanced Techniques
Lesson 4.1: Advanced Concepts and Strategies
Lesson 4.2: Performance and Optimization
Lesson 4.3: Professional-Grade Workflows
Lesson 4.4: Handling Edge Cases and Complexity
Unit 5: Machine Learning in the Real World
Lesson 5.1: Industry Standards and Conventions
Lesson 5.2: Collaboration and Team Workflows
Lesson 5.3: Portfolio and Professional Presentation
Lesson 5.4: Staying Current and Continuing Growth
Sample lesson preview
What Is Machine Learning and Why Learn It
Understand what Machine Learning is and why it matters.
Machine Learning is best understood through its purpose: what problem does it solve, or what need does it address? Rather than starting with a textbook definition, think about when and why people encounter Machine Learning in real life. Understanding the "why" first makes the technical details much easier to grasp. The simplest test of understanding: can you explain it in one sentence to someone who's never heard of it?