Chapter 4: Classification in AI
Learning Objectives
Understand introduction to classification
Understand types of classification algorithms
Understand practical classification with machine learning
Introduction to Classification
Classification is a fundamental concept in AI where objects are categorized based on their characteristics. It's widely used in various applications:
- Apples vs. Oranges: A classic example of binary classification
- Cat vs. Dog: Another common binary classification task
- Spam vs. Not Spam: Used in email filtering systems
Types of Classification Algorithms
There are several types of classification algorithms, each with its own approach:
- Decision Trees: Uses a tree-like model of decisions
- K-Nearest Neighbors: Classifies based on the closest training examples
- Support Vector Machines: Finds the optimal hyperplane to separate classes
- Artificial Neural Networks: Inspired by biological neural networks
Watch and Learn
Practical Classification with Machine Learning
To get started with practical classification tasks:
- Use Google Colab as an environment for training ML models
- Learn the basics of machine learning with Kaggle tutorials
- Try implementing a Cat vs. Dog classifier using Kaggle datasets
- Create a custom project following the provided walkthrough
Practice Exercise
Choose a dataset from Kaggle suitable for a classification task. Implement a simple classifier using the steps outlined in the custom project walkthrough. Compare the performance of at least two different classification algorithms on your chosen dataset.