Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

Author 
Brett Lantz 
ISBN10 
9781784394523 
Year 
20150731 
Pages 
452 
Language 
en 
Publisher 
Packt Publishing Ltd 
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Updated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of realworld problems datawranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.

Author 
Ethem Alpaydin 
ISBN10 
9780262028189 
Year 
20140829 
Pages 
640 
Language 
en 
Publisher 
MIT Press 
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The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semiparametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

Author 
Cory Lesmeister 
ISBN10 
9781783984534 
Year 
20151028 
Pages 
400 
Language 
en 
Publisher 
Packt Publishing Ltd 
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Master machine learning techniques with R to deliver insights for complex projects About This Book Get to grips with the application of Machine Learning methods using an extensive set of R packages Understand the benefits and potential pitfalls of using machine learning methods Implement the numerous powerful features offered by R with this comprehensive guide to building an independent Rbased ML system Who This Book Is For If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful. What You Will Learn Gain deep insights to learn the applications of machine learning tools to the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Familiarize yourself with fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Realize why and how to apply unsupervised learning methods In Detail Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a crossplatform, zerocost statistical programming environment—there has never been a better time to start applying machine learning to your data. The book starts with introduction to CrossIndustry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and realworld solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages. Style and approach This is a book explains complicated concepts with easy to follow theory and realworld, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.

Author 
Cory Lesmeister 
ISBN10 
9781787284487 
Year 
20170531 
Pages 
410 
Language 
en 
Publisher 
Packt Publishing Ltd 
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This book is for data science professionals, data analysts, or anyone with a working knowledge of machine learning, with R who now want to take their skills to the next level and become an expert in the field.

Author 
Gareth James 
ISBN10 
9781461471387 
Year 
20130624 
Pages 
426 
Language 
en 
Publisher 
Springer Science & Business Media 
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, treebased methods, support vector machines, clustering, and more. Color graphics and realworld examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors cowrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and nonstatisticians alike who wish to use cuttingedge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Author 
Stephen Marsland 
ISBN10 
1420067192 
Year 
20110323 
Pages 
406 
Language 
en 
Publisher 
CRC Press 
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Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text. Theory Backed up by Practical Examples The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve. Highlights a Range of Disciplines and Applications Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.

Author 
Valentine Fontama 
ISBN10 
9781484212004 
Year 
20150826 
Pages 
291 
Language 
en 
Publisher 
Apress 
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Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models. The authors use task oriented descriptions and concrete endtoend examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services. Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft. What’s New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration – a new feature announced February 2015 Data preparation and feature selection Data visualization with Power BI Recommendation engines Selling your models on Azure Marketplace

Author 
Raghav Bali 
ISBN10 
9781784392635 
Year 
20160331 
Pages 
340 
Language 
en 
Publisher 
Packt Publishing Ltd 
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Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated realworld problems successfully About This Book Get to grips with the concepts of machine learning through exciting realworld examples Visualize and solve complex problems by using powerpacked R constructs and its robust packages for machine learning Learn to build your own machine learning system with this examplebased practical guide Who This Book Is For If you are interested in mining useful information from data using stateoftheart techniques to make datadriven decisions, this is a goto guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary. What You Will Learn Utilize the power of R to handle data extraction, manipulation, and exploration techniques Use R to visualize data spread across multiple dimensions and extract useful features Explore the underlying mathematical and logical concepts that drive machine learning algorithms Dive deep into the world of analytics to predict situations correctly Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Write reusable code and build complete machine learning systems from the ground up Solve interesting realworld problems using machine learning and R as the journey unfolds Harness the power of robust and optimized R packages to work on projects that solve realworld problems in machine learning and data science In Detail Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them datadriven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve realworld data problems. This book takes you on a datadriven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle realworld problems. You'll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of ecommerce, finance, and socialmedia, which are at the very core of this datadriven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms. Through this book, you will learn to apply the concepts of machine learning to deal with datarelated problems and solve them using the powerful yet simple language, R. Style and approach The book is an enticing journey that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real realworld problem involving handson work thus giving you a deep insight into the world of machine learning.

Author 
Karthik Ramasubramanian 
ISBN10 
9781484223345 
Year 
20161222 
Pages 
566 
Language 
en 
Publisher 
Apress 
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Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a machine learning algorithm and use it to build a ML model from raw data. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. For every machine learning algorithm covered in this book, a 3D approach of theory, casestudy and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All the images are available in color and hires as part of the code download. This new paradigm of teaching machine learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to exampledriven learning provided in this book makes it easy for someone to connect the dots.. What You'll Learn Use the model building process flow Apply theoretical aspects of machine learning Review industrybased cae studies Understand ML algorithms using R Build machine learning models using Apache Hadoop and Spark Who This Book is For Data scientists, data science professionals and researchers in academia who want to understand the nuances of machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.

Author 
Michele Usuelli 
ISBN10 
9781783987757 
Year 
20141128 
Pages 
218 
Language 
en 
Publisher 
Packt Publishing Ltd 
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If you want to learn how to develop effective machine learning solutions to your business problems in R, this book is for you. It would be helpful to have a bit of familiarity with basic objectoriented programming concepts, but no prior experience is required.

Author 
YuWei, Chiu (David Chiu) 
ISBN10 
9781783982059 
Year 
20150326 
Pages 
442 
Language 
en 
Publisher 
Packt Publishing Ltd 
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The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create highlevel graphics. This book covers the basics of R by setting up a userfriendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.

Author 
Luis Pedro Coelho 
ISBN10 
9781784392888 
Year 
20150326 
Pages 
326 
Language 
en 
Publisher 
Packt Publishing Ltd 
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This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.

Author 
Pierre Baldi 
ISBN10 
026202506X 
Year 
2001 
Pages 
452 
Language 
en 
Publisher 
MIT Press 
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A guide to machine learning approaches and their application to the analysis of biological data.

Author 
Simon Rogers 
ISBN10 
9781439824146 
Year 
20111025 
Pages 
305 
Language 
en 
Publisher 
CRC Press 
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A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail. Referenced throughout the text and available on a supporting website (http://bit.ly/firstcourseml), an extensive collection of MATLAB®/Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems. Requiring minimal mathematical prerequisites, the classroomtested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail.