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The MIT Press Essential Knowledge series offers accessible, concise, beautifully produced pocket-size books on topics of current interest. Written by leading thinkers, the books in this series deliver expert overviews of subjects that range from the cultural and the historical to the scientific and the technical. In today’s era of instant information gratification, we have ready access to opinions, rationalizations, and superficial descriptions. Much harder to come by is the foundational knowledge that informs a principled understanding of the world. Essential Knowledge books fill that need. Synthesizing specialized subject matter for nonspecialists and engaging critical topics through fundamentals, each of these compact volumes offers readers a point of access to complex ideas.

This book is for every Java developer who wants to write quality code. This book talks about a lot of best practices that quite often are missed by developers while coding. The book also covers many design patterns. Design patterns are nothing but best practices to solve particular problems that have been tried and tested by a developer community.

This book is concerned with the concept, the theories, and the implementations of text mining. In Part I, we provide the fundamental knowledge about the text mining tasks, such as text preprocessing and text association. In Parts II and III, we describe the representative approaches to text mining tasks, provide guides for implementing the text mining systems by presenting the source codes in Java, and explain the schemes of evaluating them. In Part IV, we cover other text mining tasks, such as text summarization and egmentation, and the composite text mining tasks. Overall, in this book, we explain the text mining tasks in the functional view, describe the main approaches, and provide guidance for implementing systems, mainly about text categorization and clustering.

This book is aimed at experienced application developers, data engineers,tech enthusiasts, and others who are seeking a well-rounded understanding of the modern database landscape. Prior database experience is not strictly required, but it helps. Each chapter is designed to be taken as a long weekend’s worth of work, split up into three days. Each day ends with exercises that expand on the topics and concepts just introduced, and each chapter culminates in a wrap-up discussion that summarizes the good and bad points about the database. You may choose to move a little faster or slower, but it’s important to grasp each day’s concepts before continuing. We’ve tried to craft examples that exploreeach database’s distinguishing features. To really understand what these databases have to offer, you have to spend some time using them, and that means rolling up your sleeves and doing some work.

机器学习与预测分析正在改变企业和其他组织的运作方式，本书将带领读者进入预测分析的世界。全书共13章，除了简要介绍机器学习及Python在机器学习中的应用，还系统讲述了数据分类、数据预处理、模型优化、集成学习、回归、聚类、神经网络、深度学习等内容。本书将机器学习背后的基本理论与应用实践联系起来，通过这种方式让读者聚焦于如何正确地提出问题、解决问题。本书讲解了如何使用Python的核心元素以及强大的机器学习库，同时还展示了如何正确使用一系列统计模型。本书可作为学习数据科学的初学者及想进一步拓展数据科学领域认识的读者的参考书。同样，本书也适合计算机等相关专业的本科生、研究生阅读。

本书从Docker基本原理开始，深入浅出地讲解Docker的构建与操作，内容系统全面，可帮助开发人员、运维人员快速部署Docker应用。本书分为四大部分：基础入门、实战案例、进阶技能、开源项目，第一部分（第1～8章）介绍Docker与虚拟化技术的基本概念，包括安装、镜像、容器、仓库、数据卷，端口映射等；第二部分（第9～16章）通过案例介绍Docker的应用方法，包括与各种操作系统平台、SSH服务的镜像、Web服务器与应用、数据库的应用、各类编程语言的接口、容器云等，还介绍了作者在容器实战中的思考与经验总结；第三部分（第17～21章）是一些进阶技能，如Docker核心技术实现原理、安全、高级网络配置、libernetwork插件化网络功能等；第四部分（第22～28章）介绍与容器开发相关的开源项目，包括Etcd、Docker Machine、Docker Compose、Docker Swarm、Mesos、Kubernetes等。 第2版参照Docker技术的*新进展对全书内容进行了修订，并增加了第四部分专门介绍与容器相关的知名开源项目，利用好这些优秀的开源平台，可以更好地在生产实践中受益。

This cookbook is for data science professionals, developers, technical data analysts, and programmers who want to execute technical coding, visualize output, and do scientific computing with one tool. Prior understanding of data science concepts will be helpful for using this book, but it's not mandatory.

Learning Regular Expressions will teach you the regular expressions that you really need to know, starting with simple text matches and working p to more complex topics, including the use of backreferences, conditional evaluation, and look-ahead processing. You’ll learn what you can use, and you’ll learn it methodically, systematically, and simply by using clear and practical examples and solving real problems.

JSON is an established standard format used to exchange data. This book shows how JSON plays different roles in full web development using various examples. By the end of this book, you'll have a new perspective on providing solutions to your applications and handling complexity.

The book begins by quickly laying down the principles of enterprise data architecture and showing how they are related to the Apache Hadoop ecosystem. You will get a complete understanding of data life cycle management with Hadoop, followed by modeling structured and unstructured data in Hadoop. The book will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, as well as building efficient enterprise search solutions using tools such as Elasticsearch. You will build enterprise-grade analytics solutions on Hadoop and learn how to visualize your data using tools such as Tableau and Python. This book also covers techniques for deploying your big data solutions onpremise and on the cloud, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert big data systems that cater to any data or insight requirements, leveraging the full suite of modern big data frameworks and tools. You will have the necessary skills and know-how to become a true big data expert.

This book shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to the third-party packages Seaborn, pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you'll embed and customize your plots in third-party tools such as GTK+, Qt 5, and WXWIDGETS. You'll also be able to tweak the look and feel of your visualization with the help of the practical examples provided in this book. Further on, you'll explore Matplotlib 2.1.x on the web from a cloud-based platform using third-party packages such as Flask and Django. Finally, you will integrate interactive, real-time visualization techniques into your current workflow with the help of practical real-world examples. By the end of this book, you'll be thoroughly comfortable with using the popular Python data visualization library Matplotlib 2.1.x, and leverage its power to build attractive, insightful, and powerful visualizations.

First, you'll learn about Linux shells and why we chose the bash shell. Then, you'll learn how to write a simple bash script and how to edit your bash script using Linux editors. Following this, you will learn how to define a variable and the visibility of a variable. After this, you will learn how to store command execution output into a variable, which is called command substitution. Also, you will learn how to debug your code using bash options and Visual Studio Code. You will learn how to make your bash script interactive to the user by accepting input from the user using the read command. Then, you will learn how to read options and its values if the user passed them to the script. Following this, you will learn how to write conditional statements such as if statements and how to use case statements. After this, you will learn how to create code snippets using vim and Visual Studio Code. For repetitive tasks, you will see how to write for loops, how to iterate over simple values, and how to iterate over directory content. Also, you will learn how to write nested loops. Along with this, you will write while and until loops. Then, we will move on to functions, the reusable chunks of code. You will learn how to write functions and how to use them. After this, you will be introduced to one of the best tools in Linux, which is Stream Editor. As we are still talking about text processing, we will introduce AWK, one of the best text processing tools in Linux that you will ever see. After this, you will learn how to empower your text processing skills by writing better regular expressions. Finally, you will be introduced to Python as an alternative to bash scripting.

mobi版本，the book is for web developers, programmers,enterprise programmers,engineers and big scientis.

Data Wrangling with Pandas, NumPy, and IPython