Naïve Bayes

🎓 Naïve Bayes — Complete Tutorial | ML Lecture 32 In this video, I explain the Naïve Bayes classification algorithm from scratch — starting with what the NAME means, all the way to Python implementation! 📌 What you will learn: ✅ What "Naïve" means — the independence assumption (all variables treated independently!) ✅ What "Bayes" means — Bayes' Theorem explained simply ✅ Probability, Independent Events & Conditional Probability ✅ The full Naïve Bayes formula step by step ✅ All 3 variants — Bernoulli, Multinomial & Gaussian NB ✅ Step by step manual calculation (Tennis example) ✅ Gaussian NB implementation in sklearn — 96.67% accuracy on Iris dataset! ⏱ Timestamps: 0:00 Introduction 0:30 What does Naïve Bayes mean? 1:30 Bayes' Theorem explained 2:30 The Naïve independence assumption 3:30 Full NB formula 4:30 Probability refresher 5:30 Conditional Probability 6:30 Why use Naïve Bayes? 7:30 3 Variants — Bernoulli, Multinomial, Gaussian 8:30 Step by step calculation 9:30 sklearn implementation 10:30 Quiz time! 11:30 Summary 🛠 Tools used: Python · sklearn · Jupyter Notebook 🙏 Special thanks to Ajay Sir & AI friends Smarty and Claudi Part of my Machine Learning series — like, subscribe and share if this helped you!