DMCT-NOTES/unit 2/01_Introduction_to_ML.md

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.