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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. Don’t forget to like and subscribe to stay updated with more AI content! #MachineLearning #AIwithRoy #SupervisedLearning ~~~~~~~ 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/...