Supervised Learning I: Linear and Logistic Regression: the focus will be on the most commonly used supervised learning algorithms, Linear and Logistic Regression.
Supervised Learning II: Naive Bayes, SVM, KNN and Decision Trees: the focus will be on popular supervised algorithms such as the Naive Bayes Classifier, Support Vector Machines, K-Nearest Neighbors and Decision Trees.
Featuring Engineering: the focus will be on feature engineering methods like filter and wrapper methods, regularization and tree-based feature importance.
Unsupervised Learning: the focus will be on unsupervised learning algorithms such as K-Means Clustering, Principal Component Analysis and Hierarchical Clustering.
Improving Machine Learning Models: the focus will be on intermediate-level machine learning topics such as hyperparameter tuning, ensembling and recommender systems.