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
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