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  • Book

  • Machine Learning with R: Expert techniques for predictive modeling

    • Authors: Brett Lantz (2019)

  • 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.
  • Book

  • An Introduction to Machine Learning

    • Authors: Miroslav Kubat (2015)

  • This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.
  • Book

  • Starting out with C++. From control structures through objects

    • Authors: Tony Gaddis (2017)

  • His books help beginning students understand the important details necessary to become skilled programmers at an introductory level. Gaddis motivates the study of both programming skills and the C++ programming language by presenting all the details needed to understand the "how" and the "why"-but never losing sight of the fact that most beginners struggle with this material.
  • Book

  • Introducing data science : big data, machine learning, and more, using Python tools

    • Authors: Davy Cielen (2016)

  • Introducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You'll explore data visualization, graph databases, the use of NoSQL, and the data science process. You'll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels"--Back cover
  • Book

  • Machine Learning for OpenCV

    • Authors: Michael Beyeler (2017)

  • This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks.
  • Book

  • Adobe Illustrator CC : 2019 release

    • Authors: Brian Wood (2019)

  • Creative professionals seeking the fastest, easiest, most comprehensive way to learn Adobe Illustrator CC (2019 release) choose Adobe Illustrator CC Classroom in a Book (2019 release) from the best-selling series of hands-on software training workbooks from Adobe Press. The 15 project-based lessons show users the key techniques for realizing your creative potential with Adobe Illustrator CC. Following hands-on step-by-step projects, users create logos, illustrations, posters, and more. Explore the new streamlined Tools panel and all of the features essential for being creative in Illustrator, including working with the Shaper tool and Live Shapes along with dynamic symbols to streamline graphics creation. Create website assets and export them in multiple formats to support modern re...
  • Book

  • Wireless sensor networks

    • Authors: Ian F. Akyildiz (2010)

  • This book presents an in-depth study on the recent advances in Wireless Sensor Networks (WSNs). The authors describe the existing WSN applications and discuss the research efforts being undertaken in this field. Theoretical analysis and factors influencing protocol design are also highlighted