fundamentals of machine learning for predictive data analytics algorithms worked examples and case studies mit press

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Fundamentals Of Machine Learning For Predictive Data Analytics

Author : John D. Kelleher
ISBN : 9780262029445
Genre : Computers
File Size : 54. 20 MB
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A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

Fundamentals Of Machine Learning For Predictive Data Analytics

Author : John D. Kelleher
ISBN : 9780262331746
Genre : Computers
File Size : 25. 19 MB
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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.

Machine Learning

Author : Peter Flach
ISBN : 9781107096394
Genre : Computers
File Size : 81. 62 MB
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Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

Statistical And Machine Learning Data Mining

Author : Bruce Ratner
ISBN : 9781466551213
Genre : Business & Economics
File Size : 25. 40 MB
Format : PDF
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The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible — its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Principles Of Data Mining

Author : D. J. Hand
ISBN : 026208290X
Genre : Computers
File Size : 21. 27 MB
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The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

Machine Learning

Author : Kevin P. Murphy
ISBN : 9780262018029
Genre : Computers
File Size : 28. 29 MB
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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Understanding Machine Learning

Author : Shai Shalev-Shwartz
ISBN : 9781107057135
Genre : Computers
File Size : 44. 12 MB
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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Foundations Of Statistical Natural Language Processing

Author : Christopher D. Manning
ISBN : 0262133601
Genre : Language Arts & Disciplines
File Size : 76. 76 MB
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An introduction to statistical natural language processing (NLP). The text contains the theory and algorithms needed for building NLP tools. Topics covered include: mathematical and linguistic foundations; statistical methods; collocation finding; word sense disambiguation; and probalistic parsing.

Kernel Methods For Pattern Analysis

Author : John Shawe-Taylor
ISBN : 0521813972
Genre : Computers
File Size : 88. 1 MB
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A unique account of developing topic in data mining and machine learning.

Python Machine Learning Case Studies

Author : Danish Haroon
ISBN : 9781484228234
Genre : Computers
File Size : 28. 40 MB
Format : PDF, Kindle
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Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources. Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You’ll see machine learning techniques that you can use to support your products and services. Moreover you’ll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs. By taking a step-by-step approach to coding in Python you’ll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems.What You Will Learn Gain insights into machine learning concepts Work on real-world applications of machine learning Learn concepts of model selection and optimization Get a hands-on overview of Python from a machine learning point of view Who This Book Is For Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.

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