Machine Learning and Deep Learning

End-to-end modelling process

• 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

Category of problems

• Regression
• Classification
• Clustering
• Dimensionality Reduction
• Natural Laguage Processing (NLP)
• Image Recognition
• Time Series Forecast
• Association Rule Learning

Machine Learning using Python scikit-learn library

• 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

Machine Learning using R

• 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

Deep Learning using Keras & Tensorflow

• 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

Big Data Machine Learning using PySpark MLlib

• Regression
• Classification
• Clustering
• Natural Laguage Processing