Practical Neural Networks & Deep Learning In R (Udemy.com)
Artificial Intelligence & Machine Learning for Practical Data Science in R
Created by: Minerva Singh
Last updated October 2021
What you will learn
- Be Able To Harness The Power Of R For Practical Data Science
- Read In Data Into The R Environment From Different Sources & Carry Out Basic Pre-processing Tasks
- Master The Theory Of Artificial Neural Networks (ANN)
- Implement ANN For Classification & Regression Problems In R
- Implement Deep Learning In R
- Learn The Usage Of The Powerful H2o Package
- Learn The Implementation Of Both ANN & DNN Using The H2o Package Of R Programming Language
How it compares
Against the other 126 rated Udemy courses in Deep Learning on CourseDuck:
- Rating4.6 out of 5higher than 65% of them
- Reviews235#100 of 127 by number of reviews
- Length5.6 hoursshorter than 74% of them
- Last updatedOctober 2021earlier than 80% of them
Course Description
YOUR COMPLETE GUIDE TO PRACTICAL 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 recurrent neural networks (RNN).
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!
NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:
You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life.
After taking this course, you’ll easily use data science packages like caret, h2o, mxnet to work with real data in R...
You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.
We will also work with real data and you will have access to all the code and data used in the course.
JOIN MY COURSE NOW!
Instructor Details
- 4.6 Rating
235 Reviews
Minerva Singh
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
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Reviews
By Ramy Aziz on 10/9/2024
* Many missing files * Slides have inconsistencies and are not homogeneous. These things matter * Displayed RStudio views are not just restricted to the demonstrated data/scripts, which not only confuses, but also inspires lack of care...
By Raveena Sarkar on 11/6/2021
The course is ideally suited for my work and I am sure it will enhance the quality of my work several notches. The instructor has been brilliant in explaining finer nuances of the subject. She is very effective and impressive in bringing home the concept.
By Rishilal Yadav on 8/26/2021
The course contains useful and valuable information. It has unlimited possibilities of practical usage. The course is most suited for my work. The instructor is simply brilliant in explaining the minutest details of the subject.
By Rajesh Kumar on 7/24/2021
Practical neural networks and deep learning in R is a course which contains valuable and important information. I find the course most suited for my work. the instructor has full knowledge of the subject , has clarity of thought and is endowed with effective communication skills.
By Anonymized User on 11/20/2020
It is a fantastic course which I find most valuable and useful for my work. The instructor has deep knowledge of the subject and has an effective delivery method which keeps one engrossed throughout.
By Swarnadeepa Chakraborty on 8/24/2020
This course has been efficiently designed to gradually introduce with the requisite packages and functions required for neural networks. My personal opinion is that it is really very easy to follow the course if you have little prior idea about neural networks.
By Steven Miller on 4/29/2020
Excellent course. I am very satisfied with the course and the explanations. Furthermore, in almost every lecture, someone has had a question similar to mine. I have been able to learn a great deal from fellow student questions and answers. I like using the built-in neural net functions of R. Thank you for the course and the time you spent on it Minerva.
By Pavel Hoq on 1/12/2020
Absolutely Perfect !! The course was very interesting, short and straight to the point. Lots of resources, information and very organized sessions! I feel much more confident with my knowledge of deep learning in R.
By Ahmet Pala on 12/8/2019
Cross Validation ile alakalı daha fazla örnek olabilirdi. Dersi anlatan hoca kullanılan yöntemlerin teorik arka planını daha detaylı anlatabilirdi. Onun dışında çok faydalı bir kurstu, teşekkürler.
By Ashish Vankudre on 6/5/2018
Very practical course with numerous data-sets & different NN packages in R. Explanation videos of some key concepts to a certain depth will help more than just code walk-through videos. More examples on RNN would make the one-stop course to learn about NN.
Quality Score
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Overall Score : 92 / 100












