What you will learn from this course?
Data Preprocessing
– Data Preprocessing in Python
– Data Preprocessing in R
Regression
– Simple Linear Regression
– Multiple Linear Regression
– Polynomial Regression
– Support Vector Regression (SVR)
– Decision Tree Regression
– Random Forest Regression
– Evaluating Regression Models Performance
– Regression Model Selection in Python
– Regression Model Selection in R
Classification
– Logistic Regression
– K-Nearest Neighbors (K-NN)
– Support Vector Machine (SVM)
– Kernel SVM
– Naive Bayes
– Decision Tree Classification
– Random Forest Classification
– Classification Model Selection in Python
– Evaluating Classification Models Performance
Clustering
– K-Means Clustering
– Hierarchical Clustering
Association Rule Learning
– Apriori
– Eclat
Reinforcement Learning
– Upper Confidence Bound (UCB)
– Thompson Sampling
Natural Language Processing
Deep Learning
– Artificial Neural Networks
– Convolutional Neural Networks
Dimensionality Reduction
– Principal Component Analysis (PCA)
– Linear Discriminant Analysis (LDA)
– Kernel PCA
Model Selection & Boosting
– XGBoost
– Bonus Lectures
Time to complete : 1 month
Cost : around $19
Course Level : Intermediate
Language : English