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

Certification : No
Time to complete : 1 month
Cost : around $19
Course Level : Intermediate
Language : English