Practical Neural Networks & Deep Learning In R
4.5 (162 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,289 students enrolled

Practical Neural Networks & Deep Learning In R

Artificial Intelligence & Machine Learning for Practical Data Science in R
4.5 (162 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,289 students enrolled
Created by Minerva Singh
Last updated 12/2019
English
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Current price: $119.99 Original price: $199.99 Discount: 40% off
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This course includes
  • 5.5 hours on-demand video
  • 2 articles
  • 49 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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
Course content
Expand all 51 lectures 05:27:10
+ INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
14 lectures 01:38:28
Data and Scripts For the Course
00:09
Read in CSV & Excel Data
09:56
Read in Data from Online HTML Tables-Part 1
04:13
Read in Data from Online HTML Tables-Part 2
07:05
Introduction to dplyr for Data Summarizing-Part 1
06:11
Introduction to dplyr for Data Summarizing-Part 2
04:44
Exploratory Data Analysis(EDA): Basic Visualizations with R
18:54
+ Introduction to Artificial Neural Networks (ANN)
11 lectures 01:12:18
Neural Network for Binary Classifications
06:51
Neural Network with PCA for Binary Classifications
03:57
Implement a Multi-Layer Perceptron (MLP) For Supervised Classification
04:45
Neural Network for Multiclass Classifications
07:04
Neural Network for Image Type Data
04:31
Multi-class Classification Using Neural Networks with caret
08:26
Neural Network for Regression
04:31
More on Neural Networks- with neuralnet
09:48
Identify Variable Importance in Neural Networks
08:49
+ Start With Deep Neural Network (DNN)
14 lectures 01:19:44
Implement a Simple DNN With "neuralnet" for Binary Classifications
08:09
Implement a Simple DNN With "deepnet" for Regression
04:15
Working with External Data in H2o
04:21
Implement an ANN with H2o For Multi-Class Supervised Classification
10:30
Implement a DNN with H2o For Multi-Class Supervised Classification
06:17
Implement a (Less Intensive) DNN with H2o For Supervised Classification
03:58
Identify Variable Importance
09:02
What Are Activation Functions?
05:50
Implement a DNN with H2o For Regression
03:51
Autoencoders for Unsupervised Learning
01:46
Autoencoders for Credit Card Fraud Detection
04:11
Use the Autoencoder Model for Anomaly Detection
05:00
Autoencoders for Unsupervised Classification
06:57
+ ANN & DNN With MXNet Package in R
7 lectures 35:26
Install MXnet in R and RStudio
03:13
MXNEt Installation Code For R
00:07
Implement an ANN Based Classification Using MXNet
08:29
Implement an ANN Based Regression Using MXNet
03:48
Implement a DNN Based Multi-Class Classification With MXNet
10:46
Evaluate Accuracy of the DNN Model
02:47
Implement MXNET via "caret"
06:16
+ Convolution Neural Networks (CNN)
5 lectures 41:14
What is a CNN?
11:25
Implement a CNN for Multi-Class Supervised Classification
08:31
More About Our CNN Model Accuracy
05:52
Implement CNN on Actual Images with MxNet
07:44
RNNs With Temporal Data
07:42
Requirements
  • Be Able To Operate & Install Software On A Computer
  • Prior Exposure To Common Machine Learning Terms Such As Unsupervised & Supervised Learning
  • Prior Exposure To What Neural Networks Are & What They Can Be Used For
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!

Who this course is for:
  • People Wanting To Master The R & R Studio Environment For Data Science
  • Anyone With Prior Exposure To Common Machine Learning Concepts Such As Supervised Learning
  • Students Wishing To Learn The Implementation Of Neural Networks On Real Data In R
  • Students Wishing To Learn The Implementation Of Basic Deep Learning Concepts In R