INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
  • Introduction
  • Data and Scripts For the Course
  • Installing R and R Studio
  • Read in CSV & Excel Data
  • Read in Online CSV
  • Read in Data from Online HTML Tables-Part 1
  • Read in Data from Online HTML Tables-Part 2
  • Remove Missing Values
  • More Data Cleaning
  • Introduction to dplyr for Data Summarizing-Part 1
  • Introduction to dplyr for Data Summarizing-Part 2
  • Exploratory Data Analysis(EDA): Basic Visualizations with R
  • More Exploratory Data Analysis with xda
  • Difference Between Supervised & Unsupervised Learning
Introduction to Artificial Neural Networks (ANN)
  • Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)
  • Neural Network for Binary Classifications
  • Neural Network with PCA for Binary Classifications
  • Evaluate Accuracy
  • Implement a Multi-Layer Perceptron (MLP) For Supervised Classification
  • Neural Network for Multiclass Classifications
  • Neural Network for Image Type Data
  • Multi-class Classification Using Neural Networks with caret
  • Neural Network for Regression
  • More on Neural Networks- with neuralnet
  • Identify Variable Importance in Neural Networks
Start With Deep Neural Network (DNN)
  • Implement a Simple DNN With "neuralnet" for Binary Classifications
  • Implement a Simple DNN With "deepnet" for Regression
  • A Package for DNN Modelling in R-H2o
  • Working with External Data in H2o
  • Implement an ANN with H2o For Multi-Class Supervised Classification
  • Implement a DNN with H2o For Multi-Class Supervised Classification
  • Implement a (Less Intensive) DNN with H2o For Supervised Classification
  • Identify Variable Importance
  • What Are Activation Functions?
  • Implement a DNN with H2o For Regression
  • Autoencoders for Unsupervised Learning
  • Autoencoders for Credit Card Fraud Detection
  • Use the Autoencoder Model for Anomaly Detection
  • Autoencoders for Unsupervised Classification
ANN & DNN With MXNet Package in R
  • Install MXnet in R and RStudio
  • MXNEt Installation Code For R
  • Implement an ANN Based Classification Using MXNet
  • Implement an ANN Based Regression Using MXNet
  • Implement a DNN Based Multi-Class Classification With MXNet
  • Evaluate Accuracy of the DNN Model
  • Implement MXNET via "caret"
Convolution Neural Networks (CNN)
  • What is a CNN?
  • Implement a CNN for Multi-Class Supervised Classification
  • More About Our CNN Model Accuracy
  • Implement CNN on Actual Images with MxNet
  • RNNs With Temporal Data
  • Github