ML final term syllabus:

 ML final term syllabus:

* SVM

* Clustering K-Means, K-Medoids

* Dimensionality Reduction

* Anomaly Detection 

* Decision trees, classification trees, regression trees, Boosting (AdaBoost & Gradient Boosting), hands-on AdaBoost vs Gradient Boosting performance comparison, introduction to XGBOOST

* Large-Scale Machine Learning with large datasets, GD,(SGD), Mini-Batch Gradient Descent

* Ensemble Learning

 Ensemble learning intuition, Bagging, Random Forest, Boosting (AdaBoost & Gradient Boosting – basic idea), Introduction to Boost