Machine Learning With R

Author: Brett Lantz
Publisher: Packt Publishing Ltd
ISBN: 1784394521
Size: 76.40 MB
Format: PDF, ePub
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Build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R About This Book Harness the power of R for statistical computing and data science Explore, forecast, and classify data with R Use R to apply common machine learning algorithms to real-world scenarios Who This Book Is For 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. What You Will Learn Harness the power of R to build common machine learning algorithms with real-world data science applications Get to grips with techniques in R to clean and prepare your data for analysis and visualize your results Discover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems Classify your data with Bayesian and nearest neighbour methods Predict values using R to build decision trees, rules, and support vector machines Forecast numeric values with linear regression and model your data with neural networks Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, and big data In Detail Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of R's cross-platform, zero-cost statistical programming environment, there has never been a better time to start applying machine learning to your data. Machine learning with R offers a powerful set of methods to quickly and easily gain insight from your data to both, veterans and beginners in data analytics. Want to turn your data into actionable knowledge, predict outcomes that make real impact, and have constantly developing insights? R gives you access to all the power you need to master exceptional machine learning techniques. The second edition of Machine Learning with R provides you with an introduction to the essential skills required in 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 to to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R. Style and approach How can we use machine learning to transform data into action? This book uses a series of simple steps to show you. Using practical examples, the book illustrates how to prepare data for analysis, choose a machine learning method, and measure its success.

Mastering Machine Learning With R

Author: Cory Lesmeister
Publisher: Packt Publishing Ltd
ISBN: 1787284484
Size: 78.60 MB
Format: PDF, ePub, Docs
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Master machine learning techniques with R to deliver insights in complex projects About This Book Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning Implement advanced concepts in machine learning with this example-rich guide Who This Book Is For 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. What You Will Learn Gain deep insights into the application of machine learning tools in 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 Master fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Learn how to use R in a cloud service such as Amazon In Detail This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you'll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets. Style and approach The book delivers practical and real-world solutions to problems and a variety of tasks such as complex recommendation systems. 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.

Machine Learning With R Cookbook

Author: AshishSingh Bhatia
Publisher: Packt Publishing Ltd
ISBN: 1787287807
Size: 32.85 MB
Format: PDF, ePub
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Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R code About This Book Apply R to simplify predictive modeling with short and simple code Use machine learning to solve problems ranging from small to big data Build a training and testing dataset, applying different classification methods. Who This Book Is For This book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful. What You Will Learn Create and inspect transaction datasets and perform association analysis with the Apriori algorithm Visualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithm Compare differences between each regression method to discover how they solve problems Detect and impute missing values in air quality data Predict possible churn users with the classification approach Plot the autocorrelation function with time series analysis Use the Cox proportional hazards model for survival analysis Implement the clustering method to segment customer data Compress images with the dimension reduction method Incorporate R and Hadoop to solve machine learning problems on big data In Detail Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier. Style and approach This is an easy-to-follow guide packed with hands-on examples of machine learning tasks. Each topic includes step-by-step instructions on tackling difficulties faced when applying R to machine learning.

Deep Learning With R

Author: Francois Chollet
Publisher: Pearson Professional
ISBN: 9781617295546
Size: 73.88 MB
Format: PDF, ePub, Mobi
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Introduces deep learning systems using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.

Machine Learning With R

Author: Abhijit Ghatak
Publisher: Springer
ISBN: 9811068089
Size: 60.80 MB
Format: PDF, ePub
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This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it’s applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning. In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation. The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.

Machine Learning With R Cookbook Second Edition

Author: Ashishsingh Bhatia
Publisher:
ISBN: 9781787284395
Size: 27.45 MB
Format: PDF
View: 7705
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Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R codeAbout This Book* Apply R to simplify predictive modeling with short and simple code* Use machine learning to solve problems ranging from small to big data* Build a training and testing dataset, applying different classification methods.Who This Book Is ForThis book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful.What You Will Learn* Create and inspect transaction datasets and perform association analysis with the Apriori algorithm* Visualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithm* Compare differences between each regression method to discover how they solve problems* Detect and impute missing values in air quality data* Predict possible churn users with the classification approach* Plot the autocorrelation function with time series analysis* Use the Cox proportional hazards model for survival analysis* Implement the clustering method to segment customer data* Compress images with the dimension reduction method* Incorporate R and Hadoop to solve machine learning problems on big dataIn DetailBig data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.Style and approachThis is an easy-to-follow guide packed with hands-on examples of machine learning tasks. Each topic includes step-by-step instructions on tackling difficulties faced when applying R to machine learning.

R Unleash Machine Learning Techniques

Author: Raghav Bali
Publisher: Packt Publishing Ltd
ISBN: 1787128288
Size: 66.10 MB
Format: PDF, ePub
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Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. About This Book Build your confidence with R and find out how to solve a huge range of data-related problems Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today Don't just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis Who This Book Is For Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science What You Will Learn Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Solve interesting real-world problems using machine learning and R as the journey unfolds Write reusable code and build complete machine learning systems from the ground up Learn specialized machine learning techniques for text mining, social network data, big data, and more Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more In Detail R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it... This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems. For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media. This Learning Path has been curated from three Packt products: R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar Machine Learning with R Learning - Second Edition By Brett Lantz Mastering Machine Learning with R By Cory Lesmeister Style and approach This is an enticing learning path 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-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.

Machine Learning Using R

Author: Karthik Ramasubramanian
Publisher: Apress
ISBN: 1484242157
Size: 49.58 MB
Format: PDF, ePub
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Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R. As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning. What You'll Learn Understand machine learning algorithms using R Master the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithms See industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R 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 in practice using R.

Scala For Machine Learning

Author: Patrick R. Nicolas
Publisher: Packt Publishing Ltd
ISBN: 178712620X
Size: 42.76 MB
Format: PDF, ePub, Docs
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Leverage Scala and Machine Learning to study and construct systems that can learn from data About This Book Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and updated source code in Scala Take your expertise in Scala programming to the next level by creating and customizing AI applications Experiment with different techniques and evaluate their benefits and limitations using real-world applications in a tutorial style Who This Book Is For If you're a data scientist or a data analyst with a fundamental knowledge of Scala who wants to learn and implement various Machine learning techniques, this book is for you. All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book! What You Will Learn Build dynamic workflows for scientific computing Leverage open source libraries to extract patterns from time series Write your own classification, clustering, or evolutionary algorithm Perform relative performance tuning and evaluation of Spark Master probabilistic models for sequential data Experiment with advanced techniques such as regularization and kernelization Dive into neural networks and some deep learning architecture Apply some basic multiarm-bandit algorithms Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters Apply key learning strategies to a technical analysis of financial markets In Detail The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies. The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits. You start by learning data preprocessing and filtering techniques. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naive Bayes, hidden Markov models and Monte Carlo inference. Further, it covers the discriminative algorithms such as linear, logistic regression with regularization, kernelization, support vector machines, neural networks, and deep learning. You'll move on to evolutionary computing, multibandit algorithms, and reinforcement learning. Finally, the book includes a comprehensive overview of parallel computing in Scala and Akka followed by a description of Apache Spark and its ML library. With updated codes based on the latest version of Scala and comprehensive examples, this book will ensure that you have more than just a solid fundamental knowledge in machine learning with Scala. Style and approach This book is designed as a tutorial with hands-on exercises using technical analysis of financial markets and corporate data. The approach of each chapter is such that it allows you to understand key concepts easily.

R Deep Learning Essentials

Author: Mark Hodnett
Publisher: Packt Publishing Ltd
ISBN: 1788997808
Size: 70.86 MB
Format: PDF, ePub, Mobi
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Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and text Apply deep learning techniques in cloud for large-scale processing Build, train, and optimize neural network models on a range of datasets Book Description Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects. What you will learn Build shallow neural network prediction models Prevent models from overfitting the data to improve generalizability Explore techniques for finding the best hyperparameters for deep learning models Create NLP models using Keras and TensorFlow in R Use deep learning for computer vision tasks Implement deep learning tasks, such as NLP, recommendation systems, and autoencoders Who this book is for This second edition of R Deep Learning Essentials is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. Fundamental understanding of the R language is necessary to get the most out of this book.