Part 4 - Model Tuning, Ensemble & Unsupervised Learning | Complete ML Course | Sheryians AI School

Instructor - Akarsh Vyas Welcome to Part 4 of our Complete Machine Learning Series! In this session, we take your ML skills to the next level — learning how to improve model performance, explore unsupervised learning, and build even stronger models with advanced techniques. What you’ll learn: – What is Model Tuning and why it matters – Cross-Validation: Testing models the right way – Hyperparameter Tuning: Grid Search CV, Randomized Search CV – Ensemble Learning: Bagging, Boosting, Stacking explained – Random Forest Classifier: Powerful tree-based model – AdaBoost, Gradient Boosting, XGBoost: Taking boosting to the next level – What is Unsupervised Learning – Clustering Algorithms:  – K-Means Clustering + Elbow Method  – DBSCAN: Clustering any shape + outliers – Dimensionality Reduction:  – PCA (Principal Component Analysis)  – Curse of Dimensionality — why it matters – Hands-on Projects:  – K-Means Clustering on real data  – DBSCAN project — complex clusters  – PCA visualizations By the end of this video, you'll have a solid grasp of advanced ML techniques — and you'll be ready to tackle real-world data science problems with confidence. Links: 📝 Suggestion — Create your own structured notes during the video📚 My notes 🥲 — https://drive.google.com/file/d/1Xf6760AzL2hr1PKYC4VFRI0eNU6DTumZ/view?usp=sharing Code link - https://github.com/AkarshVyas/Machine_learning_part4 📌 Don’t forget to check out Part 1, Part 2 & Part 3 if you haven’t already — this is a complete series!👍 Like, share, and subscribe for more ML tutorials & hands-on projects! 00:00:00 - 00:00:55 intro 00:01:25 - 00:03:29 contents of the video 00:03:29 - 00:10:46 model tuning 00:10:46 - 00:19:02 cross validation 00:19:02 - 00:27:12 code implementation or cross validation 00:27:12 - 00:33:26 hyperparameter tuning 00:33:26 - 00:43:15 grid search cv 00:43:15 - 01:05:12 code implementation of grid search cv 01:05:12 - 01:09:49 random search cv 01:09:49 - 01:14:09 random search cv implementation 01:14:09 - 01:22:56 ensemble learning 01:22:56 - 01:27:56 stacking 01:27:56 - 01:32:00 bagging 01:32:00 - 01:34:14 boosting 01:34:14 - 01:49:54 code implementation of stacking 01:49:54 - 02:07:46 implementation of bagging 02:07:46 - 02:17:12 implementation of boosting 02:17:12 - 02:33:00 adaboost, gradient boost, xgboost 02:33:00 - 02:45:31 unsupervised learning 02:45:31 - 03:05:38 K-means clustering algorithm 03:05:38 - 03:14:27 K-means implementation 03:18:29 - 03:24:27 DB scan algorithm 03:24:27 - 03:29:53 implementation of dbscan 03:29:53 - 03:49:45 dimensionality reduction 03:49:45 - 03:55:02 implementation of PCA for dimensionality reduction 03:55:02 - 03:59:16 some final words 03:59:16 - 04:00:09 outro