Machine Learning (Advanced)

Course Content	Course Hours	Course Fee	Register
Section: 1 -------------------- Part 1: Clustering -------------------- 1. Welcome to Part 4 - Clustering Section: 2 K-Means Clustering 2. K-Means Clustering Intuition 3. K-Means Random Initialization Trap 4. K-Means Selecting The Number Of Clusters 5. How to get the dataset 6. K-Means Clustering in Python 7. K-Means Clustering in R Section: 3 Hierarchical Clustering 8. Hierarchical Clustering Intuition 9. Hierarchical Clustering How Dendrograms Work 10. Hierarchical Clustering Using Dendrograms 11. How to get the dataset 12. Hierarchical Clustering in Python 13. Hierarchical Clustering in R 14. Conclusion of Part 4 - Clustering Section: 4 -------------------- Part 2: Association Rule Learning ------- 15. Welcome to Part 5 - Association Rule Learning Section: 5 Apriori 16. Apriori Intuition 17. How to get the dataset 18. Apriori in R 19. Apriori in Python Eclat 20. Eclat Intuition 21. How to get the dataset 22. Eclat in R Section: 6 -------------------- Part 3: Reinforcement Learning ------ 23. Welcome to Part 6 - Reinforcement Learning Section: 7 Upper Confidence Bound (UCB) 24. The Multi-Armed Bandit Problem 25. Upper Confidence Bound (UCB) Intuition 26. How to get the dataset 27. Upper Confidence Bound in Python 28. Upper Confidence Bound in R Section: 8 Thompson Sampling 29. Thompson Sampling Intuition 30. Algorithm Comparison: UCB vs Thompson Sampling 31. How to get the dataset 32. Thompson Sampling in Python 33. Thompson Sampling in R	20 Hours	₹ 4200 | $70	Join now