Machine Learning Full Course with Projects (2025) | Machine Learning Tutorial in One Video - Part-2

At 00:00:00, the video introduces machine learning. At 00:01:46, it covers decision tree classification, followed by a practical session at 00:24:24. At 00:46:23, it discusses pre and post pruning in decision trees. At 00:57:41, decision tree regression is presented. At 01:10:58, K-nearest neighbours classification is introduced, with a practical example at 01:22:03. At 01:37:25, K-nearest neighbours regression is covered. At 01:48:06, support vector machines classification is presented, with a practical session at 02:09:25, and regression at 02:24:55. At 02:39:52, model parameters, hyperparameters, and tuning are discussed, followed by a practical grid search session at 02:54:07. At 03:07:39, cross-validation is explained, with a practical example at 03:25:46. At 03:37:17, unsupervised learning is introduced, followed by k-means clustering at 03:44:04 and a practical session at 04:02:27. At 04:21:53, hierarchical clustering is covered, with an agglomerative practical at 04:43:15. At 04:57:58, DBSCAN clustering is presented, with a practical at 05:07:38. At 05:17:33, silhouette score is discussed. At 05:35:19, association rule learning is introduced, with Apriori algorithm learning at 05:52:18 and a practical at 06:07:18. At 06:33:19, frequent pattern growth algorithm is covered, with a practical at 06:48:34. At 06:57:41, ensemble learning is introduced, followed by max voting, averaging, and weighted average voting at 07:05:11, with practical sessions for regression at 07:15:34 and classification at 07:32:24. At 07:44:19, bagging and random forest are introduced, with a practical classification session at 07:52:54 and a regression session at 08:02:38.