• Part 0 - Data Preprocessing o Missing Data o Categorical Data o Template For Preprocessing Data (General Steps)
• Part 1 - Regression o Simple Linear Regression o Multiple Linear Regression o Polynomial regression o Support Vector Regression o Decision Tree Regression o Random Forest Regression o Evaluating Regression Model o Regularisation Methods
• Part 2 - Classification o Logistic Regression o K-Nearest Neighbors (K-NN) o Support Vector Machine (SVM) o Kernel SVM o Naive Bayes o Decision Tree Classification o Random Forest Classification o Evaluating Classification Model
• Part 3 - Clustering o K-Means Clustering o Hierarchical Clustering
• Part 4 - Association Rule Learning o Apriori o Eclat
• Part 5 - Natural Language Processing o Natural Language Processing Decision Tree Random Forest Max Entropy
• Part 6 - Dimensionality Reduction o Principal Component Analysis o Linear Discriminant Analysis o kernel PCA
• Part 7 - Model Selection & Boosting o Model Selection o XGBoost