To score a job in data science, machine learning, computer graphics, or cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 300 exercises and mini-projects, this book unlocks the door to inte...(展开全部)

To score a job in data science, machine learning, computer graphics, or cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 300 exercises and mini-projects, this book unlocks the door to inte...(展开全部)

Fletcher Dunn，是著名游戏开发公司Terminal Reality的主要开发人员，所参与开发的游戏包括《4×4DVO 2）、《夜曲》（Noturne），并且是《吸血莱恩》（BloodRayne）的主要负责人。他所开发的游戏遍及家用PC机的Windows、Machintosh、Dreamcast、PSⅡ、Xbox和GameCube几种主流平台。 Ian Parberry,是北德克萨斯大学计算机科学系的教授，在国际上被公认为是教授DirectX游戏开发的顶级专家之一。

Fletcher Dunn，是著名游戏开发公司Terminal Reality的主要开发人员，所参与开发的游戏包括《4×4DVO 2）、《夜曲》（Noturne），并且是《吸血莱恩》（BloodRayne）的主要负责人。他所开发的游戏遍及家用PC机的Windows、Machintosh、Dreamcast、PSⅡ、Xbox和GameCube几种主流平台。 Ian Parberry,是北德克萨斯大学计算机科学系的教授，在国际上被公认为是教授DirectX游戏开发的顶级专家之一。

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machin...(展开全部)

This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical met...(展开全部)

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 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.

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。