These slides are explained about classification and types of classification

1. Classification

2. CLASSIFICATION
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Classification is a fundamental concept in data science and machine
learning.
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Classification is a supervised learning technique in machine learning
where the goal is to predict the category (label/class) of new data
points based on patterns learned from past data.
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Example:

Spam Email Detection: Email text → Spam / Not Spam

Medical Diagnosis: Patient data → Disease / No Disease

Image Recognition: Image → Cat / Dog / Bird

3. Types of Classification

4. Types of Classification
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Binary Classification, This is the simplest case, where
each input is assigned to one of two classes.
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The data is labeled in a binary way (e.g., 0/1, true/false,
positive/negative).

5. Types of Classification

Multi-Class Classification, Here, there are more than two
possible classes, but still exactly one label per example.

The model must pick one class out of many.

6. Types of Classification

Multi-Label Classification, In some tasks, each instance
can belong to multiple classes simultaneously. This is
different from multi-class, since examples are not
exclusive to one class i.e.Multiple labels per input

7. Types of Classification

Imbalanced Classification, Many real-world datasets are
imbalanced, meaning some classes have many more
examples than others i.e. unequal class distribution

8. Classification Algorithm