High-Yield Notes: Introduction to Machine Learning
Introduction
Machine learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn from
data and improve
their performance over time without being explicitly programmed. It is widely applied in various
industries, including
healthcare, finance, and technology.
Key Concepts
1. Supervised Learning:
- Algorithms are trained on labeled data.
- Examples: Linear regression, logistic regression, decision trees.
- Applications: Email spam detection, house price prediction.
2. Unsupervised Learning:
- Algorithms analyze and group data without labeled outputs.
- Examples: K-means clustering, principal component analysis (PCA).
- Applications: Market segmentation, customer behavior analysis.
3. Reinforcement Learning:
- Agents learn by interacting with the environment and receiving feedback (rewards or penalties).
- Examples: Q-learning, deep Q-networks (DQNs).
- Applications: Robotics, gaming, and autonomous vehicles.
