Data Science


Artificial Intelligence Bootcamp in R Programming

Practical Neural Networks and Deep Learning in R

3.95 (53 reviews)


10 hours


Jun 2020

Last Update
Regular Price

What you will learn

How to build Artificial Neural Networks (ANN) in R

How to build Convolutional Neural Networks (CNN) in R

How to use H20 package in R to solve real world challenges

Read Data Into R Environment From Different Sources

Implement Pre-processing Tasks in R Environment



This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science.

In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level!


My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.

I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic .

This course will give you a robust grounding in the main aspects of practical neural networks and deep learning.

Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science...

You will go all the way from carrying out data reading & cleaning to to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R.

Among other things:

You will be introduced to powerful R-based deep learning packages such as h2o and MXNET.

You will be introduced to deep neural networks (DNN), convolution neural networks (CNN) and unsupervised methods.

You will learn how to implement convolutional neural networks (CNN)s on imagery data using the Keras framework

You will learn to apply these frameworks to real life data including credit card fraud data, tumor data, images among others for classification and regression applications.

With this course, you’ll have the keys to the entire R Neural Networks and Deep Learning Kingdom!


Artificial Intelligence Bootcamp in R Programming
Artificial Intelligence Bootcamp in R Programming
Artificial Intelligence Bootcamp in R Programming
Artificial Intelligence Bootcamp in R Programming


Welcome to AI in R course

Welcome To The Course

Install R and RStudio

Data and Code Used in the Course

Install MXnet in R and RStudio

Install Mxnet in R- Written Instructions

Install H2o

What is Keras?

Install Keras in R

Working with Real Data

Read in Data From CSV and Excel Files

Read in Data from Online HTML Tables-Part 1

Read in Data from Online HTML Tables-Part 2

Working with External Data in H2o

Remove NAs

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

What Are the Most Common Data Types We Will Encounter?

Some Theoretical Foundations

Difference Between Supervised & Unsupervised Learning

ANN Intuition

Plan of Attack

The Neuron

The Activation Function

How do Neural Networks work?

How do Neural Networks learn?

Gradient Descent

Stochastic Gradient Descent


Build Artificial Neural Networks (ANN) in R

Neural Network 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

Implement an ANN with H2o For Multi-Class Supervised Classification

Implement an ANN Based Classification Using MXNet

Implement MLP With Keras

Keras MLP On Real Data

Keras MLP For Regression

Neural Network for Regression

More on Artificial Neural Networks(ANN) - with neuralnet

Implement an ANN Based Regression Using MXNet

Identify Variable Importance in Neural Networks

Build Deep Neural Networks (DNN) in R

Implement a Simple DNN With "neuralnet" for Binary Classifications

Implement a Simple DNN With "deepnet" for Regression

Implement a DNN with H2o For Multi-Class Supervised Classification

Implement a (Less Intensive) DNN with H2o For Supervised Classification

Implement a DNN With Keras

Implement a DNN With Keras

Identify Variable Importance

Implement MXNET via "caret"

Implement a DNN with H2o For Regression

Implement a DNN with Keras For Regression

Implement DNN Regression With Keras (Real Data)

Unsupervised Classification with Deep Learning

Theory Behind Unsupervised Classification

Autoencoders for Unsupervised Learning

Autoencoders for Credit Card Fraud Detection

Use the Autoencoder Model for Anomaly Detection

Autoencoders for Unsupervised Classification

Autoencoders With Keras

Keras Autoencoders on Real Data

Stacked Autoencoder With Keras

Keras For Outlier Detection

Find the Outlier

Outlier Detection For Cancer (With Keras)

CNN Intuition

Plan of Attack

What are convolutional neural networks?

Step 1 - Convolution Operation

Step 1(b) - ReLU Layer

Step 2 - Pooling

Step 3 - Flattening

Step 4 - Full Connection


Softmax & Cross-Entropy

Practical CNN Implementation in R

Implement a CNN for Multi-Class Supervised Classification

More About Our CNN Model Accuracy

Set Up CNN With Keras

More About CNN With Keras

Implement Keras CNN On Real Images

Some More Explanations

Improve CNN Performance

Working With Textual Data

Basic Pre-Processing of Text Data

Detect Frauds Using Keras Autoencoders on Text Data

Word Embeddings For Classifying Fraud

Word Embeddings For Classifying Fraud-GloVe


Matt20 August 2020

The first issue is not letting students know which version of R or R Studio should be installed. Many of the packages included in the training are not compatible with later versions. I also found a few data sets used in the training were not available for download on the SDS site.

Volker13 April 2020

just in the welcome capture in the first 7 films allmost all installations instructions went wrong, in codes easy ")" are missing....no...her reaction "you software isn't compatible...but another user found the right command....what she want to teach if the easy installation commands are wrong

Bryan24 January 2020

Overall, this was a very good course to cover a wide variety of deep learning frameworks in R including MXNet, H2o, and Keras across a variety of different uses cases. The structure is set so that there is a limited amount of theory (which I covered in previous python courses), and more code work which gets into the nuts and bolts of how to actually use these frameworks in R. My version of R (3.6.1) was newer than the one available for MXNet so I skipped those sections; in my work H2o and Keras are far more relevant. The main models focused on both 'toy' data like MNist and some real world data. With some of the Keras models at the end, it would have been great to take it one step further beyond the model accuracy and loss and show the final use - especially in the text/fraud case.

John28 August 2019

Instructor assumes quite a bit of previous knowledge. I wouldn't recommend to a beginner. MxNet won't install on R 3.6.1.


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