Advanced Machine Learning with R: Explore the advanced topics in machine learning with R

Tim Hoolihan

AU$14.99 ~~AU$189.99~~

Video Aug 2017 1hr 32mins 1st Edition

Video

AU$14.99 ~~AU$189.99~~

Subscription

Free Trial

Renews at AU$24.99p/m

Tim Hoolihan

AU$14.99 ~~AU$189.99~~

Video Aug 2017 1hr 32mins 1st Edition

Video

AU$14.99 ~~AU$189.99~~

Subscription

Free Trial

Renews at AU$24.99p/m

Video

AU$14.99 ~~AU$189.99~~

Subscription

Free Trial

Renews at AU$24.99p/m

What do you get with a video?

Download this video in MP4 format

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

View table of contents

Download Code

Key benefits

Dive into the advanced algorithms such as hyper-parameter tuning and deep learning, and putting your models into production
Practical, solid, real-world examples that will help you get acquainted with the various stages of machine learning using the R language
Explore important machine learning concepts such as neural network, hyper parameter, unsupervised learning

Description

Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. The R language is widely used among statisticians and data miners to develop statistical software and data analysis. Machine Learning is a cross-functional domain that uses concepts from statistics, math, software engineering, and more. In this course, you’ll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, you’ll learn all about neural networks through an example of DNA classification data. You’ll explore networks, implement them, and classify them. After that, you’ll see how to tune hyper-parameters using a data set of sonar data and you’ll get to know their properties. Next, you’ll understand unsupervised learning with an example of clustering politicians, where you’ll explore new patterns, understand unsupervised learning, and visualize and cluster the data. Moving on, we discuss some of the details of putting a model into a production system so you can use it as a part of a larger application. Finally, we’ll offer some suggestions for those who wish to practice the concepts further.

Who is this book for?

If you are an aspiring data scientist who is familiar with the basic of the R language, data frames, and some basic knowledge in statistics, this video is for you. Readers are not expected to have any knowledge of the development of Artificial Intelligence or Machine Learning systems. If you want to understand how the R programming environment and packages can be used to develop machine learning systems, then this is the perfect course for you.

What you will learn

Work with advanced techniques in machine learning with R
Explore advanced techniques such as hyper parameter tuning and deep learning
Work with Neural Networks (NNs) and explore, implement, and classify documents
Get to know hyper-parameter tuning by exploring and iterating through parameters
Understand unsupervised learning, clustering data, and visualizing
Know how to evaluate the performance of your models and put your model into use
Work with a variety of real-world algorithms that suit your problem

What do you get with a video?