Overview
Naive Bayes is a family of probabilistic algorithms based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
K-Nearest Neighbors (KNN) is a simple, instance-based learning algorithm that stores all available cases and classifies new cases based on a similarity measure.
Key Concepts
- Bayes' theorem and conditional probability
- Types of Naive Bayes classifiers
- Distance metrics in KNN
- Choosing the optimal K value
- Strengths and weaknesses of each algorithm
Practice Exercise
Exercise: Text Classification and Image Recognition
Complete two mini-projects:
- Use Naive Bayes for spam email classification
- Implement KNN for handwritten digit recognition (MNIST dataset)
- Compare the performance of both algorithms on their respective tasks
- Experiment with different parameters and preprocessing techniques
- Discuss when each algorithm would be most appropriate
Resources
Khan Academy
Main resource for today
Naive Bayes Classifier
Detailed explanation with examples
KNN Algorithm
Comprehensive guide to KNN
Text Classification with Naive Bayes
Step-by-step tutorial
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