Mastering Machine Learning with R

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.

Type
ebook
Category
Data
publication date
2019-01-31
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

no of pages
354
duration
708
key features
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 *
approach
The book delivers practical and real-world solutions to common problems faced in the machine learning domain. By the end of this book, you will have gained expertise in performing R machine learning and will be able to build complex machine learning projects using R and its packages.
audience
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.
meta description
Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications
short description
Machine learning is a field of AI where we build systems that learn from data. This book explains complicated concepts with real-world applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints. Finally, it will walk you through topics such as text analysis, time series, and deep learning.
subtitle
Advanced machine learning techniques for building smart applications with R 3.5
keywords
Machine Learning, R 3.5 , Deep learning , Statistical programming
Product ISBN
9781789618006