README.md

Machine Learning and Deep Learning

Data loading and preprocessing: missing data, visualizing, scaling, text/image preprocessing, etc
Algorithm or architecture selection
Model evaluation: train-test split, cross validation, metrics, etc
Hyperparameter tuning
Final model saving to disk and loading

Regression/Classification: Linear Regression, Ridge, Lasso, Elastic Net, Logistic Regression, Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Bagged Trees, Extra Trees, Random Forest, AdaBoost, Gradient Boost, XGBoost, Neural Network
Clustering: K-Means clustering, Hierarchical clustering
Dimensionality Reduction: Principal Component Analysis (PCA), kernel PCA, LDA
Natural Laguage Processing
Time Series Forecast: Persistence Model, Autoregressive Integrated Moving Average (ARIMA) Model
Association Rule Learning

Regression/Classification: Linear Regression, Partial Least Squares, Ridge, Lasso, Elastic Net, Logistic Regression, LDA, NB, KNN, SVM, Decision Tree, Bagged Trees, Random Forest, Gradient Boost, Multivariate Adaptive Regression Splines (MARS), Learning Vector Quantization (LVQ), Neural Network
Clustering: K-Means clustering, Hierarchical clustering
Dimensionality Reduction: PCA, kernel PCA, LDA
Natural Laguage Processing
Association Rule Learning: Apriori, Eclat

Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Reccurent Neural Network (RNN)
Regression: Artificial Neural Network
Classification: ANN, RNN, CNN
Image Recognition: Convolutional Neural Network
Natural Laguage Processing: ANN, RNN, CNN
Time Series Forecast: Reccurent Neural Network