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Artificial Intelligence Bootcamp in R Programming

Practical Neural Networks and Deep Learning in R
(53 reviews)
616 students
Created by


CourseMarks Score®







Platform: Udemy
Price: $17.99
Video: 9h 59m
Language: English
Next start: On Demand

Top Artificial Intelligence courses:

Detailed Analysis

CourseMarks Score®

8.4 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

8.7 / 10
This course was last updated on 6/2020.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

6.7 / 10
We analyzed factors such as the rating (3.6/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.3 / 10
Video Score: 9.1 / 10
The course includes 9h 59m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 5 hours 31 minutes of 172 Artificial Intelligence courses on Udemy.
Detail Score: 9.4 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

4 articles.
0 resource.
0 exercise.
0 tests or quizzes.

Table of contents



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!

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


• Knowledge how to install packages on your PC
• Basic understanding in Machine Learning Terms such as Unsupervised & Supervised Learning
• Basic knowledge in Neural Networks

This course is for

• Data Scientist and Machine Learning enthusiasts who wants to add R Programming into their toolkit

How much does the Artificial Intelligence Bootcamp in R Programming course cost? Is it worth it?

The course costs $17.99. And currently there is a 82% discount on the original price of the course, which was $99.99. So you save $82 if you enroll the course now.
The average price is $14.7 of 172 Artificial Intelligence courses. So this course is 5% cheaper than the average Artificial Intelligence course on Udemy.

Does the Artificial Intelligence Bootcamp in R Programming course have a money back guarantee or refund policy?

YES, Artificial Intelligence Bootcamp in R Programming has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Artificial Intelligence Bootcamp in R Programming course, but there is a $82 discount from the original price ($99.99). So the current price is just $17.99.

Who is the instructor? Is Minerva Singh a SCAM or a TRUSTED instructor?

Minerva Singh has created 42 courses that got 14,276 reviews which are generally positive. Minerva Singh has taught 74,511 students and received a 4.4 average review out of 14,276 reviews. Depending on the information available, Minerva Singh is a TRUSTED instructor.
Bestselling Instructor & Data Scientist(Cambridge Uni)
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year’s experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.

I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).


CourseMarks Score®







Platform: Udemy
Price: $17.99
Video: 9h 59m
Language: English
Next start: On Demand

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