KNN, Naive Bayes, Decision Trees & Random Forest | Machine Learning

In this live class, we dive deep into some of the most popular classification algorithms in machine learning: K-Nearest Neighbors (KNN), Naive Bayes, Decision Trees, and Random Forest. This session covers:

How each algorithm works and where to apply them
Step-by-step walkthrough of implementing them in Python
Comparison of their strengths and weaknesses
Evaluation metrics for classification: accuracy, precision, recall, F1-score, and more!
If you're a beginner or intermediate looking to level up your machine learning game, this is the perfect session for you.

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~~~~~~~ Timestamps ~~~~~~~


0:00 - Introduction
3:17 - K-Nearest Neighbors Theory
8:27 - kNN code implementation
14:52 - Naive Bayes
21:30 - Naive Bayes code in NLP - Movie review classification
26:09 - Naive Bayes Summary
27:16 - Evaluation Metrics (Accuracy, Precision, Recall, F1)
30:28 - Decision Trees
37:00 - Random Forest
40:36 - Decision Trees & Random Forest Code

~~~~~~~ End ~~~~~~~

Link to the code: https://github.com/souvikr/ai/

Check out my Notion website for a curated list of material and joining the AI learners WhatsApp community: https://azure-liquid-d23.notion.site/...