# Introduction to Machine Learning

## Supervised Learning
**Supervised learning** is like teaching a computer with examples. You give the computer inputs (predictors) and the correct answers (targets). The computer learns a "map" or rule to connect the inputs to the outputs.

- **Goal**: Find a model that maps input variables to a target variable.
- **Example**: Detecting phishing emails.
  - You show the computer emails with phrases like "You have won million".
  - You tell the computer these are "Spam".
  - The computer learns to flag similar new emails as Spam.

### Types of Supervised Learning
There are two main types of problems:
1. **Regression**: Predicting a number (e.g., predicting house prices).
2. **Classification**: Predicting a category or label (e.g., Spam vs Not Spam).

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## Classification
In classification, the target variable is a **category** (also called a class label).

**Example**:
- Labels: Cold, Warm, Hot.
- The model maps an instance to one of these labels.

### Types of Classification

#### 1. Binary Classification
There are only **two** possible classes.
- **Examples**:
  - Email: Spam or Not Spam.
  - Loan: Approve or Reject.
  - Medical: Disease or No Disease.
  - Exam: Pass or Fail.

#### 2. Multiclass Classification
There are **more than two** classes.
- **Examples**:
  - Digit Recognition: 0, 1, 2, ..., 9 (10 classes).
  - Fruit: Apple, Banana, Mango, Orange.
  - Movie Genre: Action, Comedy, Drama, Horror.
  - Sentiment: Very Negative, Negative, Neutral, Positive, Very Positive.