Practical Neural Networks & Deep Learning In R

Practical Neural Networks & Deep Learning In R, available at $54.99, has an average rating of 5, with 53 lectures, based on 231 reviews, and has 1893 subscribers.

You will learn about 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 This course is ideal for individuals who are People Wanting To Master The R & R Studio Environment For Data Science or Anyone With Prior Exposure To Common Machine Learning Concepts Such As Supervised Learning or Students Wishing To Learn The Implementation Of Neural Networks On Real Data In R or Students Wishing To Learn The Implementation Of Basic Deep Learning Concepts In R It is particularly useful for People Wanting To Master The R & R Studio Environment For Data Science or Anyone With Prior Exposure To Common Machine Learning Concepts Such As Supervised Learning or Students Wishing To Learn The Implementation Of Neural Networks On Real Data In R or Students Wishing To Learn The Implementation Of Basic Deep Learning Concepts In R.

Enroll now: Practical Neural Networks & Deep Learning In R

Summary

Title: Practical Neural Networks & Deep Learning In R

Price: $54.99

Average Rating: 5

Number of Lectures: 53

Number of Published Lectures: 53

Number of Curriculum Items: 53

Number of Published Curriculum Objects: 53

Original Price: $199.99

Quality Status: approved

Status: Live

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

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Lecture 1: Introduction

Lecture 2: Data and Scripts For the Course

Lecture 3: Installing R and R Studio

Lecture 4: Read in CSV & Excel Data

Lecture 5: Read in Online CSV

Lecture 6: Read in Data from Online HTML Tables-Part 1

Lecture 7: Read in Data from Online HTML Tables-Part 2

Lecture 8: Remove Missing Values

Lecture 9: More Data Cleaning

Lecture 10: Introduction to dplyr for Data Summarizing-Part 1

Lecture 11: Introduction to dplyr for Data Summarizing-Part 2

Lecture 12: Exploratory Data Analysis(EDA): Basic Visualizations with R

Lecture 13: More Exploratory Data Analysis with xda

Lecture 14: Difference Between Supervised & Unsupervised Learning

Chapter 2: Introduction to Artificial Neural Networks (ANN)

Lecture 1: Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)

Lecture 2: Neural Network for Binary Classifications

Lecture 3: Neural Network with PCA for Binary Classifications

Lecture 4: Evaluate Accuracy

Lecture 5: Implement a Multi-Layer Perceptron (MLP) For Supervised Classification

Lecture 6: Neural Network for Multiclass Classifications

Lecture 7: Neural Network for Image Type Data

Lecture 8: Multi-class Classification Using Neural Networks with caret

Lecture 9: Neural Network for Regression

Lecture 10: More on Neural Networks- with neuralnet

Lecture 11: Identify Variable Importance in Neural Networks

Chapter 3: Start With Deep Neural Network (DNN)

Lecture 1: Implement a Simple DNN With "neuralnet" for Binary Classifications

Lecture 2: Implement a Simple DNN With "deepnet" for Regression

Lecture 3: A Package for DNN Modelling in R-H2o

Lecture 4: Working with External Data in H2o

Lecture 5: Implement an ANN with H2o For Multi-Class Supervised Classification

Lecture 6: Implement a DNN with H2o For Multi-Class Supervised Classification

Lecture 7: Implement a (Less Intensive) DNN with H2o For Supervised Classification

Lecture 8: Identify Variable Importance

Lecture 9: What Are Activation Functions?

Lecture 10: Implement a DNN with H2o For Regression

Lecture 11: Autoencoders for Unsupervised Learning

Lecture 12: Autoencoders for Credit Card Fraud Detection

Lecture 13: Use the Autoencoder Model for Anomaly Detection

Lecture 14: Autoencoders for Unsupervised Classification

Chapter 4: ANN & DNN With MXNet Package in R

Lecture 1: Install MXnet in R and RStudio

Lecture 2: MXNEt Installation Code For R

Lecture 3: Implement an ANN Based Classification Using MXNet

Lecture 4: Implement an ANN Based Regression Using MXNet

Lecture 5: Implement a DNN Based Multi-Class Classification With MXNet

Lecture 6: Evaluate Accuracy of the DNN Model

Lecture 7: Implement MXNET via "caret"

Chapter 5: Convolution Neural Networks (CNN)

Lecture 1: What is a CNN?

Lecture 2: Implement a CNN for Multi-Class Supervised Classification

Lecture 3: More About Our CNN Model Accuracy

Lecture 4: Implement CNN on Actual Images with MxNet

Lecture 5: RNNs With Temporal Data

Lecture 6: Github

Lecture 7: What Is Data Science?

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 7 votes
2 stars: 7 votes
3 stars: 30 votes
4 stars: 45 votes
5 stars: 142 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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