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Databases & Big Data - Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

Description

Book Synopsis: 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.
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.

Details

Data Mining: Practical Machine Learning Tools and Techniques is the ultimate guide for anyone looking to master the world of data mining. Whether you're a beginner or an experienced professional, this book will provide you with a thorough grounding in machine learning concepts and practical advice on how to apply these tools and techniques in real-world data mining situations.

With extensive updates and new chapters on probabilistic methods and deep learning, this fourth edition is the most comprehensive and up-to-date resource available. The authors, Witten, Frank, Hall, and Pal, have combined today's techniques with cutting-edge research to bring you the most effective data mining approaches.

What sets this book apart is its practicality. Not only does it provide a solid foundation in machine learning concepts, but it also offers concrete tips and techniques for performance improvement. You'll learn how to transform inputs and outputs in machine learning methods to optimize results and achieve success in your data mining projects.

To support your learning journey, we are proud to offer a downloadable WEKA software toolkit. This comprehensive collection of machine learning algorithms for data mining tasks is presented in an easy-to-use interactive interface. You'll have all the tools you need at your fingertips to dive into the world of data mining.

To further enhance your learning experience, we have included open access online courses that apply the material covered in the book to practical applications. These courses are designed to help you apply your newly acquired knowledge in real-world scenarios, giving you the confidence and skills to excel in your data mining endeavors.

Don't miss out on this opportunity to become a data mining expert. Visit our book companion website today and embark on a journey to unlock the power of data mining.

Disclosure: I get commissions for purchases made through links in this website