- Course type
- Paid course
- All Levels
- 8 hours
- 73 lessons
- Available on completion
- Course author
- Minerva Singh
- Be Able To Harness The Power Of R For Practical Data Science
- Master The Theory Of Artificial Neural Networks (ANN) and Deep Neural Networks (DNN)
- Implement ANN For Classification & Regression Problems In R
- Learn The Implementation Of Both ANN & DNN Using The H2o Package Of R Programming Language
- Learn The Implementation Of Both ANN & DNN Using The MxNet Package Of R Programming Language
- Introduction to Convolutional Neural Networks (CNN) For Imagery Classification
- Implement CNNs Using Keras
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!
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, keras to implement novel deep learning techniques in R. You will get your hands dirty with real life data, including real-life imagery data which you will learn to pre-process and model
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!