Machine Learning
Understand how machine learning works, the different types of learning, and how algorithms learn from data.
What is Machine Learning?
Machine Learning (ML) is a branch of AI where systems learn from data and improve their performance over time without being explicitly programmed. Instead of following hard-coded rules, ML algorithms identify patterns in data and make decisions based on those patterns.
"Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed." — Arthur Samuel (1959)
Types of Machine Learning
Supervised Learning
The algorithm learns from labelled training data — input-output pairs. The goal is to learn a mapping from inputs to outputs. Examples:
- Classification — Is this email spam or not? (Output: category)
- Regression — What will house prices be next month? (Output: value)
Unsupervised Learning
The algorithm learns from unlabelled data, finding hidden patterns and structures on its own. Examples:
- Clustering — Grouping customers by purchase behaviour.
- Dimensionality Reduction — Compressing data while preserving key information.
Reinforcement Learning
An agent learns by interacting with an environment, receiving rewards for good actions and penalties for bad ones. Used in robotics, gaming (AlphaGo), and autonomous vehicles.
Common ML Algorithms
- Linear Regression — Predict continuous values.
- Logistic Regression — Binary classification.
- Decision Trees — Tree-like model of decisions.
- Random Forest — Ensemble of decision trees. More accurate.
- Support Vector Machines (SVM) — Find optimal boundaries between classes.
- Neural Networks — Inspired by the brain. Power deep learning.
- K-Means Clustering — Group data into K clusters.
Key Concepts
- Training Data — Data used to teach the model.
- Test Data — Data used to evaluate the model's performance.
- Overfitting — Model performs well on training data but poorly on new data.
- Underfitting — Model is too simple to capture the underlying pattern.
- Feature Engineering — Selecting and transforming input variables to improve model performance.
What's Next?
Explore Data Science to learn how data is collected and prepared for ML, or check out Prompt Engineering to work with modern AI language models.