h264, yuv420p, 1280x720 | ENGLISH, aac, 44100 Hz, 2 channels, s16 | 9h 58 mn | 6.43 GB

Instructor: Minerva Singh, SuperDataScience Team

Practical Neural Networks and Deep Learning in R

Practical Neural Networks and Deep Learning in R

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

Requirements

Knowledge how to install packages on your PC

Basic understanding in Machine Learning Terms such as Unsupervised & Supervised Learning

Basic knowledge in Neural Networks

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