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Linear Regression

"An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem."

– John Tukey

It's essential that we get started with a simple yet extremely effective technique that's been used for a long time: linear regression. Albert Einstein is believed to have remarked at one time or another that things should be made as simple as possible, but no simpler. This is sage advice and a good rule of thumb in the development of algorithms for machine learning. Considering the other techniques that we'll discuss later, there's no simpler model than tried and tested linear regression, which uses the least squares approach to predict a quantitative outcome. We can consider it to be the foundation of all the methods that we'll discuss later, many of which are mere extensions. If you can...

Key benefits

Build independent machine learning (ML) systems leveraging the best features of R 3.5

Understand and apply different machine learning techniques using real-world examples

Use methods such as multi-class classification, regression, and clustering

Description

Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work.

Who is this book for?

This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine learning with R is mandatory.

What you will learn

Prepare data for machine learning methods with ease

Understand how to write production-ready code and package it for use

Produce simple and effective data visualizations for improved insights

Master advanced methods, such as Boosted Trees and deep neural networks

Use natural language processing to extract insights in relation to text

Implement tree-based classifiers, including Random Forest and Boosted Tree

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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floren25 Nov 24, 2020

Este libro está escrito con la nueva forma de programar en R basada en tidyverse. Tengo cierta familiaridad con esta forma de escribir código desde que leí «R for Data Science», de Hadley Wickham y Garrett Grolemund, en que se expone en detalle todo el asunto.El problema con la lectura de este texto es que el código suministrado por el autor en la página web que sirve de soporte a este libro está plagado de errores. Y, lo que es más enigmático para mí, allí donde el código funciona, da como resultado al ejecutarlo en R algo distinto de lo que aparece en el texto escrito. Otro detalle adicional es que, a pesar de que la tercera edición de este libro es de 2019, Lesmeister emplea paquetes de R que han caído en desuso y están obsoletos. Todo esto ha hecho que la lectura haya sido bastante frustrante. Una pena porque me las prometía felices con este libro.

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naima Jul 13, 2021

This book has the worst codes I have ever seen.

Dr. Marilou Haines May 31, 2020

This is a very unfriendly website. First you need to register to get access to the data. Then, you need to look forever to find the support tab. And once you find it, you get stuck because there is no way to finalize the step. I have a book, but no files to work with. I may just have to send the book back.

Mastering Machine Learning with R: Advanced machine learning techniques for building smart applications with R 3.5 , Third Edition

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Mastering Machine Learning with R

Linear Regression

Univariate linear regression

Multivariate linear regression

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