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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.
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools 1st Edition
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Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science.

Introduction to Machine Learning with Python: A Guide for Data Scientists 1st Edition
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Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams.
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.
Practical Machine Learning
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This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles.

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.
Real-World Machine Learning 1st Edition
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Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.

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.