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Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.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. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.
The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.
856 pages, Hardcover
First published July 24, 2015
138 people are currently reading
522 people want to read
About the author
John D. Kelleher
5Ìýbooks23ÌýfollowersJohn D. Kelleher is a Professor of Computer Science and the Academic Leader of the Information, Communication, and Entertainment Research Institute at the Dublin Institute of Technology. He is the coauthor of Fundamentals of Machine Learning for Predictive Data Analytics (MIT Press).
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Displaying 1 - 8 of 8 reviews
May 16, 2021
This is a good reference book if you want to get started on machine learning (ML) and predictive analytics and I recommend it if that is your goal. It might not be an adequate book for readers with more experience in the topic. It provides a comprehensive introduction to ML with some easy-to-follow examples on the fundamental models and techniques.
October 22, 2020
Heavy going, probably because my maths isn't up the scratch. More suited to Phd students.
June 3, 2022
The best!
Read
February 28, 2017Location: GG7 IRC
Accession No: DL028845
Accession No: DL028845
February 20, 2018
I genuinely applaud the authors for this book. Their work is highly commendable.
For a novice like me, even reading the appendices was helpful. These fundamentals are definitely key to understanding the many advanced topics throughout the book. The authors disclose other essential concepts like co-variance and correlation in the chapters as well. This mandates reading each section of the book carefully and thoroughly.
The book showcases illustrious work on CRISP-DM methodology, detailing the nuances of each phase. The authors walk through each of these five phases using real-world examples. The case studies towards the end of the book revise major portions of the book.
For example, in R language, we can just call the 'lm' function to know the intercept and the weights. This book does not abstract that from us but instead focuses on the actual implementation of the algorithms and the mathematics involved behind getting the answer. There are quite a few articles/blogs which tell us how to implement an algorithm using programming languages. But knowing the inner-working is always better.
The only question I have for the authors is why H-matrix was not introduced to find the weights in the linear regression.
I absolutely enjoyed reading this book and learned quite a lot about machine learning.
For a novice like me, even reading the appendices was helpful. These fundamentals are definitely key to understanding the many advanced topics throughout the book. The authors disclose other essential concepts like co-variance and correlation in the chapters as well. This mandates reading each section of the book carefully and thoroughly.
The book showcases illustrious work on CRISP-DM methodology, detailing the nuances of each phase. The authors walk through each of these five phases using real-world examples. The case studies towards the end of the book revise major portions of the book.
For example, in R language, we can just call the 'lm' function to know the intercept and the weights. This book does not abstract that from us but instead focuses on the actual implementation of the algorithms and the mathematics involved behind getting the answer. There are quite a few articles/blogs which tell us how to implement an algorithm using programming languages. But knowing the inner-working is always better.
The only question I have for the authors is why H-matrix was not introduced to find the weights in the linear regression.
I absolutely enjoyed reading this book and learned quite a lot about machine learning.
September 20, 2016
Here and there this volume is about as clear as mud (as when talking about Bayesian networks or k-d trees). But that's the exception rather than the rule and the writing is largely exceptionally lucid and helpful with lots of concrete details for how to get the best results including two fairly extensive case studies. (That being said, I suggest supplementing the volume with other sources when things just aren't very clear.) I have had prior exposure to machine learning but still learned a good deal. Various suggested best practices for model evaluation were especially useful—e.g. harmonic mean of class accuracy is recommended for categorical prediction because it draws more attention to smaller and thus worse values—and I'm a little embarrassed I didn't know some of them earlier.
Read this book.
Read this book.
June 11, 2016
This book is a fabulous introduction to machine learning and analytics. I recommend it to all of my students as one of the two books which they should read by the end of the course I teach on data science -- the other being Introduction to Statistical Learning. The authors covers a lot of practical information which you need to succeed in the real world, such as using the CRISP-DM workflow to ensure high quality data results, handling outliers and missing data, performing EDA. In addition, they cover the big ideas/intuition behind the core models/algorithms in machine learning and include several case studies. This is not the place to learn nitty gritty details or how to program, but it will quickly boost you up the data science learning curve.
November 19, 2017
This is an excellent overview of concepts presented in a clear, straightforward manner with very good examples. The tables, figures, charts, and graphs were also very helpful as was the use of pseudocode. I recommend this book for anyone wanting a broad overview of machine learning concepts.
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