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Practical Machine Learning with R [Student Edition]
Practical Machine Learning with R [Student Edition]

Practical Machine Learning with R [Student Edition]: Define, build, and evaluate machine learning models for real world applications

Course Aug 2019 267 pages 1st Edition
Course Aug 2019 267 pages 1st Edition

Key benefits

  • Gain a comprehensive overview of different machine learning techniques
  • Explore various methods for selecting a particular algorithm
  • Implement a machine learning project from problem definition through to the final model

Description

With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the course, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this course, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it.

Who is this book for?

If you are a data analyst, data scientist, or a business analyst who wants to understand the process of machine learning and apply it to a real dataset using R, this course is just what you need. Data scientists who use Python and want to implement their machine learning solutions using R will also find this course very useful. The course will also enable novice programmers to start their journey in data science. Basic knowledge of any programming language is all you need to get started.

What you will learn

  • Define a problem that can be solved by training a machine learning model
  • Obtain, verify and clean data before transforming it into the correct format for use
  • Perform exploratory analysis and extract features from data
  • Build models for neural net, linear and non-linear regression, classification, and clustering
  • Evaluate the performance of a model with the right metrics
  • Implement a classification problem using the neural net package
  • Employ a decision tree using the random forest library

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 30, 2019
Length: 267 pages
Edition : 1st
Language : English
ISBN-13 : 9781789809350
Category :
Languages :
Tools :

Product Details

Publication date : Aug 30, 2019
Length: 267 pages
Edition : 1st
Language : English
ISBN-13 : 9781789809350
Category :
Languages :
Tools :

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