From Machine Learning to Deep Learning

• Data Preprocessing
• Feature selection
• Dimensionality Reduction (Feature extraction)
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA
• Quadratic Discriminant Analysis (QDA)

• Regression (both linear and non-linear)
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector for Regression (SVR)
• Decision Tree Classification
• Random Forest Classification

• Classification
• Logistic Regression
• K-Nearest Neighbors (K-NN)
• Support Vector Machine (SVM)
• Kernel SVM
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification

• 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 for Regression and Classification
• Convolutional Neural Networks for Computer Vision
• Recurrent Neural Networks for Time Series Analysis
• Self Organizing Maps for Feature Extraction
• Deep Boltzmann Machines for Recommendation Systems
• Auto Encoders for Recommendation Systems