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Applied Supervised Learning with R
Applied Supervised Learning with R

Applied Supervised Learning with R: Use machine learning libraries of R to build models that solve business problems and predict future trends

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Profile Icon Karthik Ramasubramanian Profile Icon Jojo Moolayil
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Can$44.98 Can$49.99
eBook May 2019 502 pages 1st Edition
eBook
Can$44.98 Can$49.99
Paperback
Can$61.99
Subscription
Free Trial
Arrow left icon
Profile Icon Karthik Ramasubramanian Profile Icon Jojo Moolayil
Arrow right icon
Can$44.98 Can$49.99
eBook May 2019 502 pages 1st Edition
eBook
Can$44.98 Can$49.99
Paperback
Can$61.99
Subscription
Free Trial
eBook
Can$44.98 Can$49.99
Paperback
Can$61.99
Subscription
Free Trial

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Applied Supervised Learning with R

Chapter 1. R for Advanced Analytics

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Explain advanced R programming constructs

  • Print the summary statistics of a real-world dataset

  • Read data from CSV, text, and JSON files

  • Write R markdown files for code reproducibility

  • Explain R data structures such as data.frame, data.table, lists, arrays, and matrices

  • Implement the cbind, rbind, merge, reshape, aggregate, and apply functions

  • Use packages such as dplyr, plyr, caret, tm, and many more

  • Create visualizations using ggplot

Note

In this chapter, we will set the foundation for programming with R and understand the various syntax and data structures for advanced analytics.

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Key benefits

  • Study supervised learning algorithms by using real-world datasets
  • Fine-tune optimal parameters with hyperparameter optimization
  • Select the best algorithm using the model evaluation framework

Description

R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.

Who is this book for?

This book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book.

What you will learn

  • Develop analytical thinking to precisely identify a business problem
  • Wrangle data with dplyr, tidyr, and reshape2
  • Visualize data with ggplot2
  • Validate your supervised machine learning model using k-fold
  • Optimize hyperparameters with grid and random search, and Bayesian optimization
  • Deploy your model on Amazon Web Services (AWS) Lambda with plumber
  • Improve your model's performance with feature selection and dimensionality reduction

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 31, 2019
Length: 502 pages
Edition : 1st
Language : English
ISBN-13 : 9781838557164
Category :
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What do you get with eBook?

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Product feature icon Download this book in EPUB and PDF formats
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OR
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Product Details

Publication date : May 31, 2019
Length: 502 pages
Edition : 1st
Language : English
ISBN-13 : 9781838557164
Category :
Languages :

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Table of Contents

9 Chapters
R for Advanced Analytics Chevron down icon Chevron up icon
Exploratory Analysis of Data Chevron down icon Chevron up icon
Introduction to Supervised Learning Chevron down icon Chevron up icon
Regression Chevron down icon Chevron up icon
Classification Chevron down icon Chevron up icon
Feature Selection and Dimensionality Reduction Chevron down icon Chevron up icon
Model Improvements Chevron down icon Chevron up icon
Model Deployment Chevron down icon Chevron up icon
Capstone Project - Based on Research Papers Chevron down icon Chevron up icon
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