Feature engineering for machine learning and data analytics,
Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you`ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
Автор: Pablo Duboue Название: The Art of Feature Engineering: Essentials for Machine Learning ISBN: 1108709389 ISBN-13(EAN): 9781108709385 Издательство: Cambridge Academ Рейтинг: Цена: 6970.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Автор: Kalita Название: Recent Developments in Machine Learning and Data Analytics ISBN: 9811312796 ISBN-13(EAN): 9789811312793 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents high-quality papers from an international forum for research on computational approaches to learning. Further, it features work that shows how to apply learning methods to solve important application problems as well as how machine learning research is conducted.
Автор: Ulf Brefeld; Jesse Davis; Jan Van Haaren; Albrecht Название: Machine Learning and Data Mining for Sports Analytics ISBN: 3030172732 ISBN-13(EAN): 9783030172732 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed post-conference proceedings of the 5th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2018, colocated with ECML/PKDD 2018, in Dublin, Ireland, in September 2018.
The 12 full papers presented together with 4 challenge papers were carefully reviewed and selected from 24 submissions. The papers present a variety of topics, covering the team sports American football, basketball, ice hockey, and soccer, as well as the individual sports cycling and martial arts. In addition, four challenge papers are included, reporting on how to predict pass receivers in soccer.
Автор: V. Sathiyamoorthi, Atilla Elci Название: Challenges and Applications of Data Analytics in Social Perspectives ISBN: 1799825671 ISBN-13(EAN): 9781799825678 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 29522.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The publication examines topics that include collaborative filtering, data visualization, and edge computing.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 179981193X ISBN-13(EAN): 9781799811930 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 27027.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 1799811921 ISBN-13(EAN): 9781799811923 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 35897.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
If you want to learn about data science and big data, then keep reading... Two manuscripts in one book:
Data Science: What You Need to Know About Data Analytics, Data Mining, Regression Analysis, Artificial Intelligence, Big Data for Business, Data Visualization, Database Querying, and Machine Learning
Big Data: A Guide to Big Data Trends, Artificial Intelligence, Machine Learning, Predictive Analytics, Internet of Things, Data Science, Data Analytics, Business Intelligence, and Data Mining
This book will discuss everything that you need to know when it comes to working in the field of data science. This world has changed, and with the modern technology that we have, it is easier than ever for companies to amass a large amount of data on the industry, on their competition, on their products, and their customers. Gathering the data is the easy part, though. Being able to sort through this data and understand what it is saying is going to be a unique challenge all on its own. This is where the process and field of data science can come in.
There is so much that we can explore and learn about when it comes to the world of data science, and this ultimate guide is here to help you navigate through these specialties. You will see just how important the ideas of data mining, data analytics, and even artificial intelligence are to our world as a whole today.
Some of the topics covered in part 1 of this book include:
What is Data Science?
What Exactly Does a Data Scientist Do?
A Look at What Data Analytics Is All About
What is Data Mining and How Does It Fit in with Data Science?
Regression Analysis
Why is Data Visualization So Important When It Comes to Understanding Your Data?
How to work with Database Querying
A Look at Artificial Intelligence
What is Machine Learning and How Is It Different from Artificial Intelligence?
What is the Future of Artificial Intelligence and Machine Learning?
And much more
Some of the topics covered in part 2 of this book include:
What is big data, and why is it important?
The five V's behind big data
How big data is already impacting your life, and where big data may be headed
How big data and your everyday devices and appliances will come together in unexpected ways via the Internet of Things
How companies and governments are using predictive analytics to get ahead of the competition or improve service
How big data is used for fraud detection
How big data can train intelligent computer systems
The many ways large corporations are benefiting from big data and the tools that use it like machine learning, AI, and predictive analytics
Upcoming trends in big data that are sure to have a large impact on your future
Artificial intelligence, and how big data drives its development
What machine learning is and how it is tied to big data
The relationship between big data, data analytics, and business intelligence
Insights into how big data impacts privacy issues
The pros and cons regarding big data
And much, much more
So if you want to learn more about data science and big data, click the "add to cart" button
Автор: O`Neil Bonnie K., Fryman Lowell Название: The Data Catalog: Sherlock Holmes Data Sleuthing for Analytics ISBN: 1634627873 ISBN-13(EAN): 9781634627870 Издательство: Gazelle Book Services Рейтинг: Цена: 8578.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Apply this definitive guide to data catalogs and select the feature set needed to empower your data citizens in their quest for faster time to insight. The data catalog may be the most important breakthrough in data management in the last decade, ranking alongside the advent of the data warehouse. The latter enabled business consumers to conduct their own analyses to obtain insights themselves. The data catalog is the next wave of this, empowering business users even further to drastically reduce time to insight, despite the rising tide of data flooding the enterprise. Use this book as a guide to provide a broad overview of the most popular Machine Learning (ML) data catalog products, and perform due diligence using the extensive features list. Consider graphical user interface (GUI) design issues such as layout and navigation, as well as scalability in terms of how the catalog will handle your current and anticipated data and metadata needs. ONeil & Frymanpresent a typology which ranges from products that focus on data lineage, curation and search, data governance, data preparation, and of course, the core capability of finding and understanding the data. The authors emphasize that machine learning is being adopted in many of these products, enabling a more elegant data democratization solution in the face of the burgeoning mountain of data that is engulfing organizations. Derek Strauss, Chairman/CEO, Gavroshe, and Former CDO, TD Ameritrade. This book is organized into three sections: Chapters 1 and 2 reveal the rationale for a data catalog and share how data scientists, data administrators, and curators fare with and without a data catalog; Chapters 3-10 present the many different types of data catalogs; Chapters 11 and 12 provide an extensive features list, current trends, and visions for the future.
Описание: ★This book includes 2 Manuscripts★Are you looking for new ways to grow your business, with resources you already have? Do you want to know how the big players like Netflix, Amazon, or Shopify use data analytics to MULTIPLY their growth? Keep listening to learn how to use data analytics to maximize YOUR business.
Описание: Use Predictive Analytics to Uncover Hidden Patterns and Correlations and Improve Decision-Making Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. In this thoroughly updated guide, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students. Delen provides a holistic approach covering key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. Balancing theory and practice, Delen presents intuitive conceptual illustrations, realistic example problems, and real-world case studies--including lessons from failed projects. It is all designed to help you gain a practical understanding you can apply for profit. * Leverage knowledge extracted via data mining to make smarter decisions * Use standardized processes and workflows to make more trustworthy predictions * Predict discrete outcomes (via classification), numeric values (via regression), and changes over time (via time-series forecasting) * Understand predictive algorithms drawn from traditional statistics and advanced machine learning * Discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
