Machine Learning Full Course with Projects (2024) | Machine Learning Tutorial in One Video - Part-2

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- 00:00:00 Machine Learning Course Intro
- 00:01:46 Decision Tree (Classification)
- 00:24:24 Decision Tree (Classification) Practical
- 00:46:23 Pre and Post Pruning in Decision Tree
- 00:57:41 Decision Tree (Regression)
- 01:10:58 K-Nearest Neighbours (Classification)
- 01:22:03 K-Nearest Neighbours (Classification) Practical
- 01:37:25 K-Nearest Neighbours (Regression)
- 01:48:06 Support Vector Machines(SVM) - Classification
- 02:09:25 Support Vector Machines(SVM) - Classification Practical
- 02:24:55 Support Vector Machines(SVM) - Regression
- 02:39:52 Model Parameter, Hyperparameter, and Tuning
- 02:54:07 GridSearchCV & RandomizedSearchCV Practical
- 03:07:39 Cross-Validation in Machine Learning
- 03:25:46 Cross-Validation in Machine Learning Practical
- 03:37:17 Unsupervised Learning
- 03:44:04 K-means Clustering
- 04:02:27 K-means Clustering Practical
- 04:21:53 Hierarchical Clustering
- 04:43:15 Agglomerative Hierarchical Practical
- 04:57:58 DBSCAN Clustering Algorithm
- 05:07:38 DBSCAN Clustering Algorithm Practical
- 05:17:33 Silhouette Score
- 05:35:19 Association Rule Learning
- 05:52:18 Apriori Algorithm Learning
- 06:07:18 Apriori Algorithm (Association Rule) Practical
- 06:33:19 Frequent Pattern Growth Algorithm (Association Rule) Learning
- 06:48:34 Frequent Pattern Growth Algorithm (Association Rule) Practical
- 06:57:41 Ensemble Learning
- 07:05:11 Max Voting, Averaging & Weighted Average Voting
- 07:15:34 Max Voting, Averaging & Weighted Average Voting (Regression Practical)
- 07:32:24 Max Voting, Averaging & Weighted Average Voting (Classification Practical)
- 07:44:19 Bagging (Bagging meta-estimator, Random forest)
- 07:52:54 Bagging (Bagging meta-estimator, Random forest) (Classification Practical)
- 08:02:38 Bagging (Bagging meta-estimator, Random forest) (Regression Practical)

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