---
product_id: 18245871
title: "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies"
price: "$286.62"
currency: USD
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reviews_count: 13
url: https://www.desertcart.ec/products/18245871-fundamentals-of-machine-learning-for-predictive-data-analytics-algorithms-worked
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---

# Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

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## Description

desertcart.com: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies: 9780262029445: Kelleher, John D., Mac Namee, Brian, D'Arcy, Aoife: Books

Review: 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.
Review: Comprehensive depth in executing CRISP-DM - If you're aware of CRISP-DM, this book will give you a comprehensive walkthrough of the process. It takes you from data exploration through to evaluation with stunning depth in a surprisingly easy to follow narrative. Kelleher uses a case study for each chapter and discusses the strengths and weaknesses of the approaches (information-, similarity-, probability, and error-based learning). There are even additional chapters dedicated to case studies taking you through the CRISP-DM process. I find that I keep on opening the book to get to the theory and to evaluate the approaches. Highly recommended reading for novices in Machine Learning wanting to get a firm grip on the process.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #473,160 in Books ( See Top 100 in Books ) #65 in Machine Theory (Books) #699 in Probability & Statistics (Books) #967 in Artificial Intelligence & Semantics |
| Customer Reviews | 4.6 4.6 out of 5 stars (122) |
| Dimensions  | 7.31 x 1.06 x 9.31 inches |
| Edition  | 1st |
| ISBN-10  | 0262029448 |
| ISBN-13  | 978-0262029445 |
| Item Weight  | 2.3 pounds |
| Language  | English |
| Print length  | 624 pages |
| Publication date  | July 24, 2015 |
| Publisher  | The MIT Press |

## Images

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## Customer Reviews

### ⭐⭐⭐⭐⭐ A must read book, in a way
*by D***R on January 31, 2020*

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.

### ⭐⭐⭐⭐⭐ Comprehensive depth in executing CRISP-DM
*by C***N on March 24, 2022*

If you're aware of CRISP-DM, this book will give you a comprehensive walkthrough of the process. It takes you from data exploration through to evaluation with stunning depth in a surprisingly easy to follow narrative. Kelleher uses a case study for each chapter and discusses the strengths and weaknesses of the approaches (information-, similarity-, probability, and error-based learning). There are even additional chapters dedicated to case studies taking you through the CRISP-DM process. I find that I keep on opening the book to get to the theory and to evaluate the approaches. Highly recommended reading for novices in Machine Learning wanting to get a firm grip on the process.

### ⭐⭐⭐⭐⭐ I wanted to have a better understanding of how the algorithms work and more importantly ...
*by S***G on March 28, 2018*

I have already used machine algorithms in production with Spark and Python, but I wanted to have a better understanding of how the algorithms work and more importantly what the variations, strengths/weaknesses, and trade-offs are for each algorithm. This book was exactly what I've been looking for. The authors explain the algorithms fluidly without any reference to specific programming libraries or languages. They introduce the concepts very well before moving into the specifics of the logic and math behind the algorithms. Following a thorough explanation of how the algorithm works, the authors then describe variants and pitfalls based on their prior foundation. So, if you aren't a math major but would like to understand the concepts and details of how ML works along with practical knowledge of variants, parameter tuning, and trade-offs, then this book should be exactly what you need. Finally, the algorithms covered are the most commonly used in ML. AI isn't covered. Look at the Table of Contents to see which algorithms are explained.

## Frequently Bought Together

- Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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*Last updated: 2026-03-25*