Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
An innovative textbook for use in advanced undergraduate and graduate courses; accessible to students in financial mathematics, financial engineering and economics.
Introduction to the Economics and Mathematics of Financial Markets fills the longstanding need for an accessible yet serious textbook treatment of financial economics. The book provides a rigorous overview of the subject, while its flexible presentation makes it suitable for use with different levels of undergraduate and graduate students. Each chapter presents mathematical models of financial problems at three different degrees of sophistication: single-period, multi-period, and continuous-time. The single-period and multi-period models require only basic calculus and an introductory probability/statistics course, while an advanced undergraduate course in probability is helpful in understanding the continuous-time models. In this way, the material is given complete coverage at different levels; the less advanced student can stop before the more sophisticated mathematics and still be able to grasp the general principles of financial economics.
The book is divided into three parts. The first part provides an introduction to basic securities and financial market organization, the concept of interest rates, the main mathematical models, and quantitative ways to measure risks and rewards. The second part treats option pricing and hedging; here and throughout the book, the authors emphasize the Martingale or probabilistic approach. Finally, the third part examines equilibrium models -- a subject often neglected by other texts in financial mathematics, but included here because of the qualitative insight it offers into the behavior of market participants and pricing.
Автор: Pyrhonen Название: Electrical Machine Drives Control - An Introduction ISBN: 1119260450 ISBN-13(EAN): 9781119260455 Издательство: Wiley Рейтинг: Цена: 15674.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This comprehensive text examines existing and emerging electrical drive technologies. The authors clearly define the most basic electrical drive concepts and go on to explain the most important details while maintaining a solid connection to the theory and design of the associated electrical machines.
Автор: Joshi Название: Introduction to Mathematical Portfolio Theory ISBN: 1107042313 ISBN-13(EAN): 9781107042315 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A concise yet comprehensive guide to the mathematics of portfolio theory from a modelling perspective, with discussion of the assumptions, limitations and implementations of the models as well as the theory underlying them. Aimed at advanced undergraduates, this book can be used for self-study or as a course text.
Автор: Boyd Stephen Название: Introduction to Applied Linear Algebra ISBN: 1316518965 ISBN-13(EAN): 9781316518960 Издательство: Cambridge Academ Рейтинг: Цена: 6811.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance.
Автор: Blyth Stephen Название: An Introduction to Quantitative Finance ISBN: 0199666598 ISBN-13(EAN): 9780199666591 Издательство: Oxford Academ Рейтинг: Цена: 6810.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The quantitative nature of complex financial transactions makes them a fascinating subject area for mathematicians of all types. This book gives an insight into financial engineering while building on introductory probability courses by detailing one of the most fascinating applications of the subject.
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.
If you're looking for a way to become an expert in machine learning, then keep reading...
Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that.
Throughout the course of this book, we're going to be covering numerous different aspects of machine learning, such as:
The different types of learning algorithm that you can expect to encounter
The numerous applications of machine learning
The future of machine learning
What neural networks and deep learning are
The best practices for picking up machine learning
What languages and libraries to work with
The different types of machine learning and how they differ
The various problems that you can solve with machine learning algorithms
And much more...
Starting from nothing, we slowly work our way through all the concepts that are central to machine learning. By the end of this book, you're going to feel as though you have an extremely firm understanding of what machine learning is, how it can be used, and most importantly, how it can change the world. You're also going to have an understanding of the logic behind the algorithms and what they aim to accomplish.
Don't waste your time working with a book that's only going to make an already complicated topic even more complicated. Pick up this book and learn everything you need to know in no time
Автор: Mitra, Sushmita , Datta, Sujay , Perkins, Theodo Название: Introduction to Machine Learning and Bioinformatics ISBN: 0367387239 ISBN-13(EAN): 9780367387235 Издательство: Taylor&Francis Рейтинг: Цена: 9798.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Lucidly Integrates Current Activities
Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other.
Examines Connections between Machine Learning & Bioinformatics
The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website.
Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems
Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today's biological experiments.
If you're looking for a way to become an expert in machine learning, then keep reading...
Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that.
Throughout the course of this book, we're going to be covering numerous different aspects of machine learning, such as:
The different types of learning algorithm that you can expect to encounter
The numerous applications of machine learning
The future of machine learning
What neural networks and deep learning are
The best practices for picking up machine learning
What languages and libraries to work with
The different types of machine learning and how they differ
The various problems that you can solve with machine learning algorithms
And much more...
Starting from nothing, we slowly work our way through all the concepts that are central to machine learning. By the end of this book, you're going to feel as though you have an extremely firm understanding of what machine learning is, how it can be used, and most importantly, how it can change the world. You're also going to have an understanding of the logic behind the algorithms and what they aim to accomplish.
Don't waste your time working with a book that's only going to make an already complicated topic even more complicated. Pick up this book and learn everything you need to know in no time
Автор: Chirag Shah Название: A Hands-On Introduction to Data Science ISBN: 1108472443 ISBN-13(EAN): 9781108472449 Издательство: Cambridge Academ Рейтинг: Цена: 7286.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A practical introduction to data science with a low barrier entry, this textbook is well-suited to students from a range of disciplines. Assuming no prior knowledge of the subject, the hands-on exercises and real-life application of popular data science tools are accessible even to students without a strong technical background.
Описание: This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for users without any prior programming knowledge.
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