Welcome to the course!
-------------------- Part 1: Data Preprocessing --------------------
-------------------- Part 2: Regression --------------------
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance
-------------------- Part 3: Classification --------------------
Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Evaluating Classification Models Performance
-------------------- Part 4: Clustering --------------------
K-Means Clustering
-------------------- Part 5: Association Rule Learning --------------------
Apriori
-------------------- Part 6: Reinforcement Learning --------------------
Upper Confidence Bound (UCB)
-------------------- Part 7: Natural Language Processing --------------------
-------------------- Part 8: Deep Learning --------------------
Artificial Neural Networks
Convolutional Neural Networks
-------------------- Part 9: Dimensionality Reduction --------------------
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Kernel PCA
-------------------- Part 10: Model Selection & Boosting --------------------
Model Selection
XGBoost