43 lines
1.5 KiB
Markdown
43 lines
1.5 KiB
Markdown
# Introduction to Machine Learning
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## Supervised Learning
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**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.
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- **Goal**: Find a model that maps input variables to a target variable.
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- **Example**: Detecting phishing emails.
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- You show the computer emails with phrases like "You have won million".
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- You tell the computer these are "Spam".
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- The computer learns to flag similar new emails as Spam.
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### Types of Supervised Learning
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There are two main types of problems:
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1. **Regression**: Predicting a number (e.g., predicting house prices).
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2. **Classification**: Predicting a category or label (e.g., Spam vs Not Spam).
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---
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## Classification
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In classification, the target variable is a **category** (also called a class label).
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**Example**:
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- Labels: Cold, Warm, Hot.
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- The model maps an instance to one of these labels.
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### Types of Classification
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#### 1. Binary Classification
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There are only **two** possible classes.
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- **Examples**:
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- Email: Spam or Not Spam.
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- Loan: Approve or Reject.
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- Medical: Disease or No Disease.
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- Exam: Pass or Fail.
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#### 2. Multiclass Classification
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There are **more than two** classes.
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- **Examples**:
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- Digit Recognition: 0, 1, 2, ..., 9 (10 classes).
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- Fruit: Apple, Banana, Mango, Orange.
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- Movie Genre: Action, Comedy, Drama, Horror.
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- Sentiment: Very Negative, Negative, Neutral, Positive, Very Positive.
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