KNN & Naive Bayes Explained | Easy ML Algorithms with Intuition and Python Examples

Описание: KNN & Naive Bayes Explained | Easy ML Algorithms with Intuition and Python Examples

Welcome to the next video in our Machine Learning tutorial series! 🎓
In this video, you will learn two beginner-friendly yet powerful classification algorithms: K-Nearest Neighbors (KNN) and Naive Bayes. This video covers complete theory, real-world intuition, and hands-on Python implementation to help you master these ML models easily.

📚 What You Will Learn:
✅ What is KNN and how does it work?
✅ Real-life intuition behind KNN (with visual examples)
✅ Choosing the right value of 'K'
✅ What is Naive Bayes algorithm?
✅ How probability and Bayes’ Theorem are used
✅ Advantages, limitations, and best use cases
✅ Python implementation using scikit-learn
✅ Practical tips for choosing between KNN and Naive Bayes

👨‍💻 Technologies Used:
Python


Jupyter Notebook


Scikit-learn


Matplotlib / Seaborn for visualizations



🎯 Perfect For:
ML beginners & students


Data science enthusiasts


College project builders


Interview preparation



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