This book is an introduction to concepts, techniques, and applications in data science. This book focuses on the analysis of data, covering concepts from statistics to machine learning, techniques for graph analysis and parallel programming, and applications such as recommender systems or sentiment analysis. All chapters introduce new concepts that are illustrated by practical cases using real data. Public databases such as Eurostat, different social networks, and MovieLens are used. Specific questions about the data are posed in each chapter. The solutions to these questions are implemented using Python programming language and presented in code boxes properly commented. This allows the reader to learn data science by solving problems which can generalize to other problems. This book is not intended to cover the whole set of data science methods neither to provide a complete collection of references. Currently, data science is an increasing and emerging field, so readers are encouraged to look for specific methods and references using keywords in the net.

Appropriate for upper-division undergraduate and graduate level courses in computer vision found in departments of computer science, computer engineering and electrical engineering, this book offers a treatment of modern computer vision methods.

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Thoughtful Machine Learning with Python: A Test-Driven Approach English | 25 Aug. 2016 | ISBN: 1491924136 | 250 Pages | AZW3/MOBI/EPUB/PDF (conv) | 16.77 MB By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment. You’ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for testing machine learning code. The author provides real-world examples to demonstrate the results of using machine-learning code effectively. Featuring graphs and highlighted code throughout, Thoughtful Machine Learning with Python guides you through the process of writing problem-solving code, and in the process teaches you how to approach problems through scientific deduction and clever algorithms.

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Machine Learning projects our ultimate desire to understand the essence of human intelligence onto the space of technology. As such, while it cannot be fully understood in the restricted field of computer science, it is not necessarily the search of clever emulations of human cognition. While diggin

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