Until you’ve consumed all of the best Data Mining books, can you even claim to be a true fan?
- Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) (2016)
- The Hundred-Page Machine Learning Book (2019)
- Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (2011)
- Introduction to Machine Learning with Python: A Guide for Data Scientists (2016)
- Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition (2019)
- Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More (2018)
- Data Mining for the Masses, Third Edition: With Implementations in RapidMiner and R (2018)
- Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (2018)
- Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning (2018)
- Learning Data Mining with Python: Use Python to manipulate data and build predictive models, 2nd Edition (2017)
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (2017)
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.
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.
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.
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.
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.
Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram.
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.
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.
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.
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.
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.
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 Library 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.
Data Mining for Co-location Patterns: Principles and Applications
Author(s): Guoqing Zhou
ID: 3161313, Publisher: CRC Press, Year: 2022, Size: 12 Mb, Format: pdf
Preference-based Spatial Co-location Pattern Mining (Big Data Management)
Author(s): Lizhen Wang, Yuan Fang, Lihua Zhou
ID: 3194211, Publisher: Springer, Year: 2022, Size: 14 Mb, Format: pdf
Advanced Data Mining Tools and Methods for Social Computing
Author(s): Sourav De, Sandip Dey, Siddhartha Bhattacharyya, Surbhi Bhatia
ID: 3200796, Publisher: Academic Press, Year: 2022, Size: 4 Mb, Format: epub
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.