MACHINE LEARNING (2/1 CREDITS)

Learning Outcomes:

On successful completion of this course, students will be able to: explain  concepts and challenges of machine learning systems; demonstrate feature engineering  techniques on the dataset to get important features for modelling; construct supervised and unsupervised learning  models using python for solving  a given problem; examine the best machine learning model for a given problem.

Topics:

1. Machine Learning Landscape;
2. End-to-End Machine Learning Project;
3. Classification;
4. Training Models;
5. Support Vector Machines;
6. Decision Trees;
7. Esemble Learning;
8. Dimensionality Reduction;
9. Unsupervised Learning;
10. Classification;
11. Training Models;
12. Support Vector Machine;
13. Ensemble Learning;
14. Unsupervised Learning.