Practical Machine Learning with R

Jeyaraman, Brindha Priyadarshini, Olsen, Ludvig Renbo, Wambugu, Monicah

  • 出版商: Packt Publishing
  • 出版日期: 2019-08-30
  • 售價: $1,160
  • 貴賓價: 9.5$1,102
  • 語言: 英文
  • 頁數: 416
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838550135
  • ISBN-13: 9781838550134
  • 相關分類: Machine Learning 機器學習
  • 下單後立即進貨 (約1~2週)

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商品描述

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 book, 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 book, 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.

  • 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
  • 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