作者简介： Willi Richert 机器学习和机器人学博士，目前任职于微软Bing搜索核心研发团队。他从事多种机器学习领域的研究，包括主动学习和统计机器翻译。 Luis Pedro Coelho 计算生物学家，主要关注生物图像信息学和大规模图像数据的处理，致力于生物标本图像分析中机器学习技术的应用，他还是Python计算机视觉库mahotas的主要开发人员。他于1998年开始开发开源软件，2004年起从事Python开发，并为多个Python开源库贡献了代码。另外，Luis拥有机器学习领域世界领先的卡内基-梅隆大学的博士学位，并发表过多篇科学论文。 译者简介： 刘峰 百度LBS地图基础业务部资深研发工程师，新加坡南洋理工大学计算机工程系博士，研究领域包括机器学习、模糊神经网络等。2010年加入百度，主要从事大数据分析和挖掘方面的工作，近年来专注于...(展开全部)

Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems.Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you’ll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems.By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks.

Willi Richert 机器学习和机器人学博士，目前任职于微软Bing搜索核心研发团队。他从事多种机器学习领域的研究，包括主动学习和统计机器翻译。 Luis Pedro Coelho 计算生物学家，主要关注生物图像信息学和大规模图像数据的处理，致力于生物标本图像分析中机器学习技术的应用，他还是Python计算机视觉库mahotas的主要开发人员。他于1998年开始开发开源软件，2004年起从事Python开发，并为多个Python开源库贡献了代码。另外，Luis拥有机器学习领域世界领先的卡内基-梅隆大学的博士学位，并发表过多篇科学论文。

This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to provide Machine Learning support to their existing projects, and see them get implemented effectively .Computer science researchers, data scientists, Artificial Intelligence programmers, and statistical programmers would equally gain from this book and would learn about effective implementation through lots of the practical examples discussed.Readers need no prior experience with Machine Learning or statistical processing. Python development experience is assumed.

Willi Richert 机器学习和机器人学博士，目前任职于微软Bing搜索核心研发团队。他从事多种机器学习领域的研究，包括主动学习和统计机器翻译。 Luis Pedro Coelho 计算生物学家，主要关注生物图像信息学和大规模图像数据的处理，致力于生物标本图像分析中机器学习技术的应用，他还是Python计算机视觉库mahotas的主要开发人员。他于1998年开始开发开源软件，2004年起从事Python开发，并为多个Python开源库贡献了代码。另外，Luis拥有机器学习领域世界领先的卡内基-梅隆大学的博士学位，并发表过多篇科学论文。

This book is for data scientists, scientists, or just the curious. To get the most out of this book, you will need to know some linear algebra and some Python, and have a basic knowledge of machine learning concepts.

This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning, and how to utilize the TensorFlow library to rapidly build powerful ML models. You’ll learn the basics of regression, classification, and clustering algorithms, applying them to solve real-world challenges such as call center volume prediction and sen...(展开全部)

Python是流行、开源、面向对象的编程语言，既可以用于编写单机运行的程序也可以编写脚本程序，具有可移植、强大、易用等特点。跟随专业教师学习是最快捷的掌握此语言的方式。新版的《学习Python》把你带到Mark Lutz和David Ascher面前，他们是著名的Python专家和培训师，他们的用语亲切、组织良好的文章已经指导了许多编程者精通这种语言。 《学习Python（第二版）》给程序员提供了一个学习Python和面向对象编程的综合学习工具。它根据1999年该书第一版发布以来Python语言的大量变化做了更新。本书介绍了最近发布的Python 2.3的基本要素并涵盖了新的特性，如列表内涵，嵌套作用域和迭代器/产生器。 除了语言特性，《学习Python（第二版）》还为初级程序员提供了新的内容，包括新的面向对象编程和动态类型回顾，新的关于编程起步...(展开全部)

Key Features Bored of too much theory on TensorFlow? This book is what you need! Thirteen solid projects and four examples teach you how to implement TensorFlow in production. This example-rich guide teaches you how to perform highly accurate and efficient numerical computing with TensorFlow It is a practical and methodically explained guide that allows you to apply Tensorflow’...(展开全部)

If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.