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The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining.
If you want to learn how to analyze your data with R, this is your book. A broad range of real-world case studies highlights the breadth and... Læs mere
This book explains the world of database systems to bioinformatics and cheminformatics database users. It begins with a survey of popular databases, including primary... Læs mere
The ultimate objective of this book is to provide the academic and industrial communities with an in-depth introduction to the recent advances in development,... Læs mere
This book provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner,... Læs mere
This book describes a novel approach to scientific research that uses unstructured data analysis as a generative tool for... Læs mere
Reinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several... Læs mere
Digital Image Processing with C++ presents the theory of digital image processing, and implementations of... Læs mere
With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.
This snapshot of the current frontier of statistics and network analysis focuses on the foundational topics of... Læs mere