Udemy Machine Learning A-Z

By:Kirill Eremenko

[Data Scientist & Forex Systems Expert]

Content

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
•Hierarchical Clustering

Part 5:Association Rule Learning

•Apriori
•Eclat

Part 6:Reinforcement Learning

•Upper Confidence Bound (UCB)
•Thompson Sampling

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