Naive Bayes Explained

Lecture description
If you want to know:
• What is the Naive Bayes algorithm and how does it work?
• How can Naive Bayes be applied to classification problems?
• What are the key components of the Naive Bayes theorem?
• How do you calculate probabilities using Naive Bayes?
• What are the advantages of using Naive Bayes for machine learning?
Then this lecture is for you!
Dive into the world of probabilistic classification with this comprehensive lecture on the Naive Bayes algorithm. Learn how this powerful machine learning technique uses Bayes' theorem to make predictions based on prior probabilities. Discover the step-by-step process of applying Naive Bayes to real-world classification problems, including calculating prior probabilities, likelihoods, and posterior probabilities. Explore practical examples using age and salary data to predict whether someone walks or drives to work. Gain insights into the algorithm's strengths, including its simplicity and effectiveness in text classification and sentiment analysis tasks. By the end of this lecture, you'll have a solid understanding of Naive Bayes classifiers and be ready to implement them in your own machine learning projects using Python.
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42:22:49 of on-demand video • Updated March 2025