Introduction to Machine Learning

📋 Course Outline

1. Machine Learning Definition
2. History Milestones
3. Supervised Learning
4. Unsupervised Learning
5. Reinforcement Learning
6. Features and Labels
7. Training and Testing Data
8. Overfitting and Underfitting
9. Linear Regression
10. Decision Trees
11. Support Vector Machines
12. Neural Networks

📖 1. Machine Learning Definition

🔑 Key Concepts & Definitions

• Machine Learning (ML): A subset of artificial intelligence that enables computers to learn from data patterns and make decisions or predictions without explicit programming.

• Algorithm: A step-by-step procedure or set of rules used by ML models to analyze data and identify patterns.

• Model: The mathematical or computational representation trained by an algorithm on data, used to make predictions or classifications.

• Features: Input variables or attributes used by the model to make predictions (e.g., age, income).

• Labels: The output or target variable that the model aims to predict or classify (e.g., spam or not spam).

• Training Data: A dataset used to teach the model by adjusting its parameters based on input-output pairs.

📝 Essential Points

• Machine learning systems learn from data rather than relying on explicit instructions for each task.

• It encompasses various types, including supervised, unsupervised, and reinforcement learning, each suited for different problems.