Biznes IT
Machine Learning with R Cookbook. Analyze data and build predictive models - Second Edition
AshishSingh Bhatia, Yu-Wei, Chiu (David Chiu)
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.
Machine Learning with R. Expert techniques for predictive modeling - Third Edition
Brett Lantz
Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
Iván Pastor Sanz
Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline.From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling.By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R.
Brett Lantz
Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of big data and data science. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data.Machine Learning with R is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions.How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.Machine Learning with R will provide you with the analytical tools you need to quickly gain insight from complex data.
Md. Rezaul Karim
Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala.The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms.It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.
Kevin Jolly
Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.
Kevin Jolly
Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.
Rajdeep Dua, Manpreet Singh Ghotra
This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.
Machine Learning with Swift. Artificial Intelligence for iOS
Alexander Sosnovshchenko, Oleksandr Baiev
Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Rich Collier, Bahaaldine Azarmi
Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data.As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure.By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.
Rich Collier, Bahaaldine Azarmi
Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data.As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure.By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.
Rich Collier, Camilla Montonen, Bahaaldine Azarmi
Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection.The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with.By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.
Allan MacGregor, 10505331 CANADA INC
Magia Instagrama. Jak zdobyć milionowe zasięgi w 90 dni
Mirosław Skwarek
A Ty, ilu chcesz mieć obserwujących na Instagramie? Instagram. Medium społecznościowe, które jest z nami już od kilkunastu lat, właśnie przeżywa boom. W rytmie, w jakim zamiera Facebook, Instagram rośnie, docierając do coraz większej liczby osób. Prosty, fotograficzny lub filmowy przekaz okraszony kilkoma słowami komentarza, oczywiście hashtagami oraz linkiem w bio. Tyle wystarczy, by dotrzeć do tzw. grupy docelowej i stać się instagramowym influencerem. Czy to naprawdę takie proste? Nie do końca. Na Instagramie obecnych jest dziś około 9 mln Polaków. Wielu użytkowników jedynie odbiera publikowane tam treści bądź incydentalnie umieszcza na swoich kontach posty. Jednak nie o takich instagramerach jest ta książka. Istnieje grono osób, które publikują tam regularnie, merytorycznie i w doskonale przemyślany sposób, budując dzięki Instagramowi ogromne zasięgi oraz markę osobistą. Te działania pozwalają często zarabiać spore pieniądze. Konkurencja jest duża, ale droga pozostaje otwarta. Jeśli zatem chcesz dołączyć do grona instagramowych influencerów, ta książka odsłoni przed Tobą ich sekrety i umożliwi Ci zdobycie tylu obserwujących, ilu zapragniesz mieć. Książka w mediach: Biblioteka Macieja Bookstagram
Magia Instagrama. Jak zdobyć milionowe zasięgi w 90 dni
Mirosław Skwarek
A Ty, ilu chcesz mieć obserwujących na Instagramie? Instagram. Medium społecznościowe, które jest z nami już od kilkunastu lat, właśnie przeżywa boom. W rytmie, w jakim zamiera Facebook, Instagram rośnie, docierając do coraz większej liczby osób. Prosty, fotograficzny lub filmowy przekaz okraszony kilkoma słowami komentarza, oczywiście hashtagami oraz linkiem w bio. Tyle wystarczy, by dotrzeć do tzw. grupy docelowej i stać się instagramowym influencerem. Czy to naprawdę takie proste? Nie do końca. Na Instagramie obecnych jest dziś około 9 mln Polaków. Wielu użytkowników jedynie odbiera publikowane tam treści bądź incydentalnie umieszcza na swoich kontach posty. Jednak nie o takich instagramerach jest ta książka. Istnieje grono osób, które publikują tam regularnie, merytorycznie i w doskonale przemyślany sposób, budując dzięki Instagramowi ogromne zasięgi oraz markę osobistą. Te działania pozwalają często zarabiać spore pieniądze. Konkurencja jest duża, ale droga pozostaje otwarta. Jeśli zatem chcesz dołączyć do grona instagramowych influencerów, ta książka odsłoni przed Tobą ich sekrety i umożliwi Ci zdobycie tylu obserwujących, ilu zapragniesz mieć. Książka w mediach: Biblioteka Macieja Bookstagram
Sudhi Ranjan Sinha
Bridge the gap between data and decision.Big Data has brought about a revolution in the way we do business. Essential business decisions can today be informed by the wealth of data now at our disposal. However, while Big Data may appear to be the answer to every business problem, for many, gaining real value from data – gaining business insights is a difficult task. Big Data, for many, is a big problem itself, with many struggling to reap the rewards that it promises. In this accessible and stimulating guide, Sudhi Sinha, management, technology and sustainability expert, provides a unique perspective on Big Data and how to derive maximum value from it – with sharp and careful analytics.“this is a perfect starter for any manager who wants to understand and explore Big Data… The Big Data field is evolving quickly, and this book serves as a quick and practical introduction to the field.”AnHai Doan, Professor, University of Wisconsin; Chief Scientist at WalmartLabs USAThis insightful and engaging book demonstrates that Big Data, to be most effective, needs to be weaved within the fabric of organization strategy. Without it, you are simply left with numbers and statistics, lacking purpose – lacking potency. Beginning with the essential stage of building a strategy framework for you Big Data analytics project, and integrating it within your wider business strategy, Sudhi provides you with the knowledge and insight to help you build a big data strategy that gets results.Big data is one of the biggest buzzwords in the world of business today. And while it is true that it has opened up huge opportunities for businesses of all sizes, it is nevertheless difficult for many businesses to turn the reserves of numbers and statistics at their disposal into clear insights that can inform important business decisions. Beginning with the creation of a Big Data strategy and the identification of the key opportunities that it has the potential to unlock, the book will then demonstrate how to implement and manage your project with the best team and the right technology for your needs. Once this is in place you will then find out how to get the most from your Big Data insights, with effective organizational alignment and change management.
Manage Your SAP Projects with SAP Activate. Implementing SAP S/4HANA
Vinay Singh
It has been a general observation that most SAP consultants and professionals are used to the conventional waterfall methodology. Traditionally, this method has been there for ages and we all grew up learning about it and started practicing it in real world. The evolution of agile methodology has revolutionized the way we manage our projects and businesses. SAP Activate is an innovative, next generation business suite that allows producing working deliverables straight away. Manage your SAP Project with SAP Activate, will take your learning to the next level. The book promises to make you understand and practice the SAP Activate Framework. The focus is to take you on a journey of all the phases of SAP Activate methodology and make you understand all the phases with real time project examples. The author explains how SAP Activate methodology can be used through real-world use cases, with a comprehensive discussion on Agile and Scrum, in the context of SAP Project.You will get familiar with SAP S4HANA which is an incredibly innovative platform for businesses which can store business data, interpret it, analyze it, process it in real time, and use it when it's needed depending upon the business requirement.