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Artificial Intelligence Bootcamp in R Programming (Udemy.com)

Practical Neural Networks and Deep Learning in R

Created by: Minerva Singh

Last updated December 2023

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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

How it compares

Against the other 205 rated Udemy courses in Artificial Intelligence on CourseDuck:

  • Rating3.8 out of 5higher than 6% of them
  • Reviews102#172 of 206 by number of reviews
  • Length10 hourslonger than 72% of them
  • Last updatedDecember 2023earlier than 69% of them

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Quality Score

Content Quality
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Course Depth & Coverage
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Overall Score : 76 / 100

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Course Description

YOUR COMPLETE GUIDE TO ARTIFICIAL NEURAL NETWORKS & DEEP LEARNING IN R:

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!

LEARN FROM AN EXPERT DATA SCIENTIST:

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!Who this course is for:
  • Data Scientist and Machine Learning enthusiasts who wants to add R Programming into their toolkit

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Instructor Details

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Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid travelerHi there,
We are the SuperDataScience Social team. You will be hearing from us when new SDS courses are released, when we publish new podcasts, blogs, share cheatsheets and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
The Real People at SuperDataScience

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Reviews

3.8

102 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Anil Kumar Pandey

Informative

By sushma Pandey

Informative and excellent

By Susan Tang

This is most detailed AI course I seen

By Monica Shah

It is good to have a deep learning course that uses R

By Posey Parker

A good mix of theory and application

By Sunny Lamba

I really enjoyed the theoretical lectures. They teach the basic ideas without the jargon

By John D Hazard

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

By Prity Polly

Very thorough deep learning course in R

By Selvia Jain

Good mix of theory and practical AI lectures

By Infine

super good material! but you need to have some little experience with R

By Bryan Butler

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.

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