After finishing the course, student should demonstrate the following skills:

1) Ability to differentiate between different learning models, and perfrome proper model evaluation and validation.

2) Ability to apply different supervided learning approaches for regression (such as linear and logistic regression).

3) Ability to apply different supervided learning approaches for classification (such as decsion trees. Naive bayes, and SVMs).

4) Ability to apply different unsupervided learning approaches for clustring (such as K-means, and KNN, and DBSCAN)

5) Ability to apply different ensemble learning approaches (such as Adaboost)

6) Ability to apply different approaches for handling imbalanced datasets (such as SMOTE and Borderline-SMOTE)

7) Ability to apply basic deep learning approaches such as FFNN, and CNN.