Naïve Bayes Step by Step Numerical Example + Python Code Explained

🚀 Master Naïve Bayes from Scratch! 🚀
In this step-by-step tutorial, we break down the Naïve Bayes algorithm in a way that’s easy to understand for beginners while still being insightful for advanced learners. Whether you're new to machine learning or looking to solidify your understanding, this video has something for everyone!

🔢 What You’ll Learn:
✅ Step-by-Step Numerical Example: We’ll walk through a real-world dataset (weather and tennis) to understand how Naïve Bayes works.
✅ Math Behind Naïve Bayes: Learn the probability theory and Bayes' Theorem that power this algorithm.
✅ Python Implementation: We’ll code the Naïve Bayes classifier from scratch in pure Python and also using scikit-learn.
✅ Practical Applications: Discover where Naïve Bayes is used in real life (e.g., spam detection, sentiment analysis).

💻 Python Code Included:
- Pure Python Implementation: No libraries, just Python!
- scikit-learn Implementation: Learn how to use the powerful `CategoricalNB` classifier.

🎯 Who Is This For?
- Beginners: No prior knowledge of machine learning? No problem! We start from the basics.
- Intermediate/Advanced Learners: Deepen your understanding with a detailed numerical example and coding walkthrough.

📊 Why Naïve Bayes?
Naïve Bayes is one of the simplest yet most powerful algorithms in machine learning. It’s fast, efficient, and works great for classification tasks like spam filtering, text classification, and more!

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