The best 18 Data Mining books

Artificial Intelligence Basics by Tom Taulli provides a comprehensive introduction to the world of AI. It covers everything from the history and evolution of AI to its current applications and potential future developments. Taulli breaks down complex concepts in a way that is accessible to all readers, making this book a great starting point for anyone interested in understanding the fundamentals of artificial intelligence.

Building Recommender Systems with Machine Learning and AI by Frank Kane provides a comprehensive guide to understanding and creating recommendation systems. It covers the fundamental concepts, various algorithms, and practical implementation using Python. Whether you are a beginner or an experienced data scientist, this book equips you with the knowledge and skills to build effective recommendation systems.

Macroanalysis by Matthew L. Jockers offers a groundbreaking exploration of the potential of big data and computational analysis in the study of literature. By analyzing vast amounts of texts, Jockers uncovers patterns and trends that provide new insights into literary history, style, and authorship. This book challenges traditional literary analysis and paves the way for a more data-driven approach to the study of literature.

Machine Learning: 3 books in 1 by Adam Bash is a comprehensive guide that covers the fundamentals of machine learning, deep learning, and neural networks. It provides practical examples and hands-on exercises to help beginners understand complex concepts and apply them in real-world scenarios. Whether you're a student, a professional, or just curious about machine learning, this book is a valuable resource to kickstart your journey in this exciting field.

Pandas for Everyone is a comprehensive guide to using the pandas library for data analysis in Python. Written by Daniel Y. Chen, this book provides clear explanations and practical examples to help readers master the fundamentals of pandas and apply them to real-world data analysis tasks. Whether you are a beginner or an experienced data analyst, this book will equip you with the knowledge and skills needed to effectively manipulate and analyze data using pandas.

Practical Time Series Analysis by Aileen Nielsen provides a comprehensive guide to analyzing time series data. It covers various techniques such as trend analysis, seasonality, and forecasting, using real-world examples and practical code examples in Python. Whether you're a beginner or an experienced data analyst, this book will help you master time series analysis and make informed decisions based on historical data.

Python for Data Analysis by Wes McKinney is a comprehensive guide that teaches you how to use Python for data analysis. It covers essential libraries such as NumPy, pandas, and matplotlib, and provides practical examples and case studies to help you understand how to manipulate, clean, and analyze data effectively. Whether you are a beginner or an experienced data analyst, this book is a valuable resource for mastering data analysis with Python.

SQL QuickStart Guide by Walter Shields is a comprehensive book designed to help beginners learn and understand SQL quickly and effectively. It covers the basics of SQL, database design, querying, and advanced topics such as joins and subqueries. With clear explanations and practical examples, this book serves as a great resource for anyone looking to master SQL.

Statistical Methods for Speech Recognition by Frederick Jelinek delves into the complex world of speech recognition and the statistical techniques used to decipher and understand human speech. The book provides a comprehensive overview of the mathematical and statistical methods employed in this field, making it a valuable resource for researchers and practitioners in speech recognition and related areas.

Supercharge Power BI by Matt Allington is a comprehensive guide that helps users harness the full potential of Power BI. It provides step-by-step instructions and real-world examples to demonstrate how to create compelling data visualizations, perform advanced data modeling, and utilize DAX formulas. Whether you're a beginner or an experienced user, this book equips you with the knowledge and skills to unlock the power of Power BI.

The Enterprise Big Data Lake by Alex Gorelik provides a comprehensive guide to understanding and implementing data lakes in enterprise environments. It covers the fundamentals of big data, the challenges of traditional data management, and the benefits of adopting a data lake architecture. With practical insights and real-world examples, this book equips business and IT leaders with the knowledge they need to leverage data lakes for improved analytics and decision-making.

The Numerati by Stephen Baker explores the growing influence of data and mathematical models in various aspects of our lives. From predicting consumer behavior to analyzing social networks, the book delves into how individuals and businesses are using data to understand and manipulate human behavior. It raises thought-provoking questions about privacy, ethics, and the implications of living in a world where our every move is tracked and analyzed.

Data Analysis with Open Source Tools by Philipp K. Janert provides a comprehensive guide to performing data analysis using open source software. It covers various tools and techniques, including data manipulation, visualization, and statistical analysis. Whether you're a beginner or an experienced data analyst, this book offers valuable insights and practical examples to help you make sense of your data.

Machine Learning with R by Brett Lantz is a comprehensive guide that introduces you to the world of machine learning using the R programming language. It covers a wide range of topics including data preprocessing, model evaluation, and various machine learning algorithms such as decision trees, random forests, and neural networks. Whether you're a beginner or an experienced R user, this book provides practical examples and hands-on exercises to help you understand and implement machine learning techniques in R.

Mining the Social Web by Matthew A. Russell is a comprehensive guide that explores how to collect, analyze, and visualize data from different social media platforms. From Twitter and Facebook to LinkedIn and GitHub, this book provides practical examples and step-by-step instructions for leveraging the power of social media data to gain valuable insights.

Neural Networks and Deep Learning by Charu C. Aggarwal delves into the intricate world of artificial neural networks and their applications in deep learning. It offers a comprehensive exploration of the underlying concepts, models, and algorithms, making it an essential read for anyone interested in understanding the cutting-edge technology shaping our future.

OCA Oracle Database SQL Exam Guide by Steve O'Hearn is a comprehensive resource for anyone preparing for the Oracle Certified Associate (OCA) certification exam. It covers all the essential topics related to SQL and database concepts, providing clear explanations, practice questions, and real-world examples to help you master the material. Whether you're a beginner or an experienced professional, this book will guide you through the exam preparation process and ensure your success.

Practical SQL by Anthony DeBarros is a comprehensive guide that takes you through the essential concepts and practical applications of SQL. Whether you're a beginner or an experienced programmer, this book provides clear explanations and real-world examples to help you master SQL and effectively manage your data.