Machine Learning Algorithms: KNN & Naive Bayes Explained with Python

🔍 Unlock the Power of KNN & Naive Bayes in Machine Learning!
In this hands-on tutorial, you’ll learn two of the most important supervised machine learning algorithms: K-Nearest Neighbors (KNN) and Naive Bayes. We’ll cover everything from theory to real-world Python implementation, all designed for beginners in ML and Data Science.

📘 What You’ll Learn:
What is K-Nearest Neighbors (KNN) and how it works

Understanding Naive Bayes and its probabilistic foundations

Differences between KNN and Naive Bayes

When to use KNN vs Naive Bayes in real-world projects

Implementing both algorithms in Python step-by-step

Real-life examples, use cases, and visualizations

🎯 Why Watch This Video?
KNN and Naive Bayes are easy to learn, highly interpretable, and commonly asked about in interviews, university courses, and real-world ML applications. Mastering them gives you a strong foundation in both distance-based and probabilistic machine learning models.

📂 Resources & Code:
👉https://github.com/MCAL-GLOBAL/MachineLearning

👨‍🏫 Perfect For:
Machine Learning & Data Science Beginners

Students learning ML with Python

Developers preparing for interviews

Anyone curious about how basic ML algorithms work under the hood

💬 Have questions or suggestions? Drop them in the comments below.
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