Course Contents
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1.Data Preprocessing
2.Regression
2.1 Simple Linear Regression
2.2 Multiple Linear Regression
2.3 Polynomial Regression
2.4 Support Vector Regression (SVR)
2.5 Decision Tree Regression
2.6 Random Forest Regression
3.Classification
3.1 Logistic Regression
3.2 K-Nearest Neighbors (K-NN)
3.3 Support Vector Machine (SVM)
3.4 Kernel SVM
3.5 Naive Bayes
3.6 Decision Tree Classification
3.7 Random Forest Classification
4.Association Rule Learning
4.1 Apriori
5.Natural Language Processing
6.Deep Learning
6.1 Artificial Neural Networks
6.2 Convolutional Neural Networks