2 edition of Mastering business math and machines found in the catalog.
Mastering business math and machines
Burton S. Kaliski
|Statement||Burton S. Kaliski, Vera G. Kinzey.|
|Contributions||Kinzey, Vera G.|
|The Physical Object|
|Number of Pages||470|
this book is to provide a view of machine learning that focuses on ideas and models, not on math. It is not possible (or even advisable) to avoid math. But math should be there to aid understanding, not hinder it. Finally, this book attempts to have minimal dependencies, so that one can fairly easily pick and choose chapters to read. WhenFile Size: 2MB. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers many more cutting-edge data mining topics. View Free Book See Reviews. Data Mining and Machine Learning. Data Mining: Practical Machine Learning Tools and Techniques.
The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. This book reflects more than 25 years of author involvement with business math education and the business community. The focus of this edition is on linking mathematics with real business practices in real businesses, giving readers a better appreciation for and understanding of the concepts that are vital in the business world.
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ISBN: OCLC Number: Notes: Draws in part from authors' textbooks, Today's business math and Mastering ten-key calculators. * Coordinate your business plan and budget * Use zero-based budgeting effectively. With Mastering Business Finance, even the least experienced number cruncher can gain confidence in understanding vital terms and making financial : Business Management Daily.
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This book is packed with real-world examples to easily use machine learning techniques in your business applications. 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.”.
Top 10 Best Business Mathematics Books | WallStreetMojo. But machine learning isn’t a solitary endeavor; it’s a team process that requires data scientists, data engineers, business analysts, and business leaders to collaborate. The power of machine learn-ing requires a collaboration so the focus is on solving business problems.
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