Udemy

Artificial Intelligence Bootcamp in R Programming

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  • 875 Students
  • Updated 1/2025
3.5
(83 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
9 Hour(s) 59 Minute(s)
Language
English
Rating
3.5
(83 Ratings)
1 views

Course Overview

Artificial Intelligence Bootcamp in R Programming

Practical Neural Networks and Deep Learning in R

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!

Course Content

  • 11 section(s)
  • 86 lecture(s)
  • Section 1 Welcome to AI in R course
  • Section 2 Working with Real Data
  • Section 3 Some Theoretical Foundations
  • Section 4 ANN Intuition
  • Section 5 Build Artificial Neural Networks (ANN) in R
  • Section 6 Build Deep Neural Networks (DNN) in R
  • Section 7 Unsupervised Classification with Deep Learning
  • Section 8 CNN Intuition
  • Section 9 Practical CNN Implementation in R
  • Section 10 Working With Textual Data
  • Section 11 Congratulations!! Don't forget your Prize :)

What You’ll 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

Reviews

  • A
    Aniruddh Kumar Mishra
    5.0

    good

  • P
    Paritosh Khare
    5.0

    A very nice course on Artificial Intelligence with good explanations.

  • B
    Bryan Butler
    4.5

    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.

  • I
    Infine
    3.5

    theoretical material is brilliant! but practical stuff is not well explained

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