Module 2: Machine Learning Basics
Welcome back! In this module, Maria Chen will teach you how AI actually learns from data. You'll discover features, labels, training data, and the different ways machines learn.
What You'll Learn:
- 📊 How machines learn from examples (not rules)
- 🔍 Features - what AI looks at to make decisions
- 🏷️ Labels - the correct answers for training
- 🎯 Three types of learning: supervised, unsupervised, reinforcement
Where Does Machine Learning Fit?
Before diving in, let's see the big picture. Click each layer to learn what it is and why it matters.
Click each layer to explore (0/4 explored)
Core ML Concepts
Now let's explore the building blocks of Machine Learning. Click each card to learn more.
Click each concept (0/4 explored)
Feature Selection Challenge
You're building an AI spam detector! Which features would help the AI identify spam emails?
Select the features that would actually help identify spam emails. Not all features are useful - choose wisely!
Instructions: Click on features you think are good indicators of spam. Find all 5 useful features to continue.
Find all 5 useful features (0/5 found)
Supervised Learning
The most common type of ML! The AI learns from examples that have correct answers (labels) attached.
Real-World Examples
Click each card to explore how supervised learning is used.
Click each example to learn about it (0/3 explored)
Unsupervised Learning
Here the AI finds patterns on its own - without being told the right answers!
Real-World Examples
Click each card to explore how unsupervised learning is used.
Click each example to learn about it (0/3 explored)
Reinforcement Learning
Here the AI learns by trial and error — making decisions and getting feedback!
Real-World Examples
Click each card to explore how reinforcement learning is used.
Click each example to learn about it (0/3 explored)
ML Paradigm Sorting Challenge
Now let's test your understanding! Sort each scenario into the correct type of machine learning.
Instructions: Click a scenario, then click the correct ML type to sort it.
Sort all 6 scenarios (0/6 sorted)
ML Vocabulary Check
Let's make sure you know the key ML terms! Match each term to its definition.
How to play: Click a term on the left, then click its matching definition on the right.
Knowledge Check
Answer these questions to earn your ML Explorer Certificate!
ML Explorer Certificate
AI Fundamentals Series - Module 2 Complete
Bronze Level
Your Name
Has demonstrated understanding of Machine Learning fundamentals including features, labels, training data, and the three ML paradigms (supervised, unsupervised, and reinforcement learning).
What's Next?
In Module 3: Neural Networks, you'll learn how artificial neurons work together to recognize patterns!