Section 1 : Welcome to the course!
Section 2 : Part 1 Data Preprocessing
Section 3 : Data Preprocessing in Python
Section 4 : Data Preprocessing in R
Section 5 : Part 2 Regression
Section 6 : Simple Linear Regression
Section 7 : Multiple Linear Regression
Section 8 : Polynomial Regression
Section 9 : Support Vector Regression (SVR)
Section 10 : xDecision Tree Regression
Section 11 : Random Forest Regression
Section 12 : Evaluating Regression Models Performance
Section 13 : Regression Model Selection in Python
Section 14 : Regression Model Selection in R
Section 15 : Part 3 Classification
Section 16 : Logistic Regression
Section 17 : K-Nearest Neighbors (K-NN)
Section 18 : Support Vector Machine (SVM)
Section 19 : Kernel SVM
Section 20 : Naive Bayes
Section 21 : Decision Tree Classification
Section 22 : Random Forest Classification
Section 23 : Classification Model Selection in Python
Section 24 : Evaluating Classification Models Performance
Section 25 : Part 4 Clustering
Section 26 : K-Means Clustering
Section 27 : Hierarchical Clustering
Section 28 : Part 5 Association Rule Learning
Section 29 : Apriori
Section 30 : Eclat
Section 31 : Part 6 Reinforcement Learning
Section 32 : Upper Confidence Bound (UCB)
Section 33 : Thompson Sampling
Section 34 : Part 7 Natural Language Processing
Section 35 : Part 8 Deep Learning
Section 36 : Artificial Neural Networks
Section 37 : Convolutional Neural Networks
Section 38 : Part 9 Dimensionality Reduction
Section 39 : Principal Component Analysis (PCA)
Section 40 : Linear Discriminant Analysis (LDA)
Section 41 : Kernel PCA
Section 42 : Part 10 Model Selection & Boosting
Section 43 : Model Selection
Section 44 : XGBoost
Section 45 : Bonus Lectures