Until you’ve consumed all of the best Data Mining books, can you even claim to be a true fan?
- 1. Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) (2016)
- 2. The Hundred-Page Machine Learning Book (2019)
- 3. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (2011)
- 4. Introduction to Machine Learning with Python: A Guide for Data Scientists (2016)
- 5. Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition (2019)
- 6. Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More (2018)
- 7. Data Mining for the Masses, Third Edition: With Implementations in RapidMiner and R (2018)
- 8. Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (2018)
- 9. Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning (2018)
- 10. Learning Data Mining with Python: Use Python to manipulate data and build predictive models, 2nd Edition (2017)
- 11. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (2017)
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1. Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) (2016)
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive…
2. The Hundred-Page Machine Learning Book (2019)
to avoid counterfeit, make sure that the book . Avoid third-party sellers. , Research Director at Google, co-author of , the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field.” ,…
3. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (2011)
The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition–more than 50% new and revised– is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice…
4. Introduction to Machine Learning with Python: A Guide for Data Scientists (2016)
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the…
5. Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition (2019)
Key Features Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer…
6. Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More (2018)
Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re talking about, and where they’re located—using Python code examples, Jupyter notebooks, or Docker containers.In part one, each standalone chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blogs and…
7. Data Mining for the Masses, Third Edition: With Implementations in RapidMiner and R (2018)
Some say we live in the Information Age; others, the Social Age; and still others, the Big Data Age. Regardless of what name we give it, we live in an age that generates monumental amounts of data—in all different kinds of formats. In business, and in our personal lives, we use smartphones and tablets, web sites and watches; with apps and interfaces to shop, learn, entertain and inform. Businesses increasingly use technology to interact with consumers to provide marketing, customer service, product information and more. All of this technological activity generates data, and we’re increasingly good at gathering, storing…
8. Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (2018)
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical…
9. Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning (2018)
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics.Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert,…
10. Learning Data Mining with Python: Use Python to manipulate data and build predictive models, 2nd Edition (2017)
Key Features Book DescriptionThis book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK.You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now.With restructured examples and code samples updated for the latest edition of…
11. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (2017)
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture…
Best Data Mining Books Everyone Should Read
We highly recommend you to buy all paper or e-books in a legal way, for example, on Amazon. But sometimes it might be a need to dig deeper beyond the shiny book cover. Before making a purchase, you can visit resources like Genesis and download some data mining books mentioned below at your own risk. Once again, we do not host any illegal or copyrighted files, but simply give our visitors a choice and hope they will make a wise decision.
Biometric Identification Technologies Based on Modern Data Mining Methods
Author(s): Stepan Bilan, Mohamed Elhoseny, D. Jude Hemanth
ID: 2890295, Publisher: Springer International Publishing;Springer, Year: 2021, Size: 10 Mb, Format: pdf
Data Visualization Guide: Clear Introduction to Data Mining, Analysis, and Visualization
Author(s): Campbell, Alex
ID: 2902446, Publisher: , Year: 2021, Size: 3 Mb, Format: epub
Predictive Data Mining Models [2nd ed.]
Author(s): David L. Olson, Desheng Wu
ID: 2396585, Publisher: Springer, Year: 2020, Size: 5 Mb, Format: pdf
Please note that this booklist is not absolute. Some books are really chart-busters according to The New York Times, others are written by unknown authors. On top of that, you can always find additional tutorials and courses on Coursera, Udemy or edX, for example. Are there any other relevant books you could recommend? Leave a comment if you have any feedback on the list.