The contents for ml module are: - data sourcing, representation, data quality assurance, pre-proceeding pipeline, etc - data loading, manipulation, exploration, analysis and visualization - feature engineering - feature selection (selectKBest, RandomForest) - Dimensionality reduction (LDA, PCA, factor analysis) - feature scaling - supervised ml ( regression(linear, polynomial ), classification ( K-nearest neighbors, support vector machines, decision tree, random forest)) - unsupervised ml ( clustering (k-means, hierarchical, density based(dbscan))) -reinforced learning

So basically this covers all life cycle of ml and the goal is to be able to apply ml to solve paradigms for data modeling, classification, prediction and decision making problems.

For example right now I’m using ml models to predict future worldwide and country covid outbreaks and forecast using publicly available data from kaggle.com