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Machine Learning for Email: Spam Filtering and Priority Inbox
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If youíre an experienced programmer willing to crunch data, this concise guide will show you how to use machine learning to work with email. Youíll learn how to write algorithms that automatically sort and redirect email based on statistical patterns
Seven Mobile Apps in Seven Weeks: Native Apps, Multiple Platforms
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Answer the question "Can we build this for ALL the devices?" with a resounding YES. Learn how to build apps using seven different platforms: Mobile Web, iOS, Android, Windows, RubyMotion, React Native, and Xamarin.
Machine Learning in Python: Essential Techniques for Predictive Analysis
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Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python.
Machine Learning with Spark
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Apache Spark is a framework for distributed computing that is designed from the ground up to be optimized for low latency tasks and in-memory data storage. It is one of the few frameworks for parallel computing that combines speed, scalability, in-memory processing, and fault tolerance with ease of programming and a flexible, expressive, and powerful API design.
Machine Learning, 1st Edition A Bayesian and Optimization Perspective
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This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models
Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data
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Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data.
Machine Learning and Medical Imaging
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It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing.