Lec 6 | Master 3 Probability Concepts Before Learning Bayes’ Theorem #probability #machinelearning

Lec 6 | Master 3 Probability Concepts Before Learning Bayes’ Theorem #probability #machinelearning Welcome to *Foundations for Machine Learning by ScholarsGT!* 🎓 In this lecture, we revise and connect three fundamental concepts of probability that form the foundation for understanding *Bayes’ Theorem* and its applications in Machine Learning. If you have been following our previous lectures, this video will help you bring everything together through clear explanations and numerical examples. Previous Lecture on Conditional Probability, Joint Probability and Total Law of Probability.    • Lec 3 | Conditional & Joint Probability fo...      • Lec 4 | Conditional Probability & Joint Pr...      • Lec 5 | Law of Total Probability | Probabi...   📚 *What You Will Learn in This Video:* ✅ *Conditional Probability* – Understand how the probability of an event changes when we have additional information. ✅ *Joint Probability* – Learn how to calculate the probability of two events occurring together. ✅ *Law of Total Probability* – Understand how to calculate the probability of an event using multiple possible cases. ✅ *Numerical Examples* – Strengthen your understanding by solving problems and connecting the concepts. ✅ *Connection to Bayes’ Theorem* – Discover how these concepts come together to make Bayes’ Theorem much easier to understand. 🚀 *Why Is This Important for Machine Learning?* Probability is not just a mathematical concept. It plays a vital role in probabilistic models, Naive Bayes classification, uncertainty estimation, and many real-world AI/ML applications. By revising these concepts now, you will be well prepared for our next lecture on **Bayes’ Theorem**, where we will build on this foundation step by step. 🎯 *Who Should Watch?* M.Tech and B.Tech students studying Machine Learning Beginners learning AI, ML, and Data Science Working professionals strengthening their mathematical foundations Anyone preparing for ML interviews or technical examinations 📌 *Next Lecture:* Bayes’ Theorem – Understanding the Formula with Numerical Examples If you find this lecture helpful, please *Like, Share, and Subscribe to ScholarsGT* for more educational content on Mathematics, Machine Learning, Deep Learning, and Cybersecurity. #MachineLearning #Probability #BayesTheorem #DataScience #FoundationsForMachineLearning #ScholarsGT