Machine Learning Basics
Discover how machines learn from data and make intelligent predictions
Your Progress
0 / 5 completed←
Previous Module
What is Artificial Intelligence?
What is Machine Learning?
Machine Learning (ML) is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed. Instead of following fixed rules, ML systems discover patterns in data and use them to make predictions or decisions.
🧠 The Core Idea
📊
1. Data
Feed examples to the system
⚙️
2. Learning
Algorithm finds patterns
🎯
3. Prediction
Make decisions on new data
Example: Email Spam Filter
→Data: Thousands of emails labeled as spam or not spam
→Learning: Algorithm learns patterns (e.g., "FREE MONEY!!!" = spam)
→Prediction: Automatically filters new emails based on learned patterns
⚙️ Traditional Programming
INPUT
Data + Rules
↓
OUTPUT
Results
Developer writes explicit rules for every scenario
🤖 Machine Learning
INPUT
Data + Results
↓
OUTPUT
Rules (Model)
Algorithm discovers rules automatically from examples
💡 Why Machine Learning?
✓
Handles Complexity
Solves problems too complex for manual rules (e.g., image recognition)
✓
Adapts Over Time
Improves as it sees more data (e.g., Netflix recommendations)
✓
Discovers Patterns
Finds insights humans might miss (e.g., fraud detection)
✓
Scales Efficiently
Handles billions of decisions per day (e.g., search ranking)