Fundamentals of Machine Learning for Predictive Data Analytics – Algorithms, Worked Examples, and Case Studies
Product ID: 56776424
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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. 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.
| Best Sellers Rank | 2,513 in Computer Information Systems |
| Dimensions | 18.57 x 2.69 x 23.65 cm |
| Isbn 10 | 0262029448 |
| Isbn 13 | 978-0262029445 |
| Item Weight | 1.04 kg |
| Language | English |
| Print Length | 624 pages |
| Publication Date | 4 Sept. 2015 |
| Publisher | MIT Press |
| Reading Age | 18 years and up |
User
Five Stars
Of all the machine learning books I have read to date, I have found this book the most digestible.I like the way the book is structured: information-based learning, similarity-based learning, probability-based learning, error-based learning.Each chapter includes exercises that help reinforce learning but are enjoyable to tackle at the same time.The case studies provide further insights and reinforce the applied approach the book takes to understanding machine learning for predictive analytics.
User
Amazing, informative, and comprehensive
Well packed. One of the most amazing, informative, and comprehensive machine learning books. Presents information in a great way and well structured with great case studiesHighly recommended!
User
Analytics; from A to Z.
I've received this book as part of The Analytics Store three day course on Fundamentals of Machine Learning. Both the book and course provide great insight into the role of a data scientist, the preparations required to carry out analytics, and the range of analytic model that can be used. There are plenty of examples and case studies to illustrate, and the material is approachable and very useful as an ongoing reference book.
User
An essential read for anyone studying ML
The fundamentals of ML are excellently presented here with fantastic real world insights and case studies which bring the concepts to life. An essential read for anyone interested in ML.
User
Structure
Good
User
One of the best books on Data Science
One of the best books on Data Science with practical examples, data sets and solutions...
User
Machine Learning made Awesome
This book is used in my uni for machine learning, very useful and gives a new perspective to machine learning
User
Introduccion a ML
Muy buen libro para iniciar en el tema de ML. Principalmente el área de prediccion Matemáticas simples
User
A must read book, in a way
Here is my opinion, none of the books out there on Machine Learning cover all the topics needed to understand basics, underlying fundamentals and also how to program using myriad frameworks out there. The trick is to find the sources(books) that complement each other in filling this need. Here is one book that explains underlying fundamentals of ML in a very simple and intuitive way for starters. This is not meant for someone that has advanced mathematical background and intuitions, but I believe they too would benefit from the clarity this book adds. Also it explains some of the topics that are not generally elaborated well and rushed in most books, for example entropy, ID3, distance metrics etc. This a good complimentary book to everything I have in my bookshelf about ML. The price point of this books definitely stings though.
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