If you are a security professional whose workload is increasing, or a Puppet professional looking to increase your knowledge of security, or even an experienced systems administrator, then this book is for you. This book will take you to the next level of security automation using Puppet. The book requires no prior knowledge of Puppet to get started.

This book is for anyone who wants to learn Intel Galileo for home automation and cross-platform software development. No knowledge of programming with Intel Galileo is assumed, but knowledge of the C programming language is essential.

If you are an Android developer looking to test your applications or optimize your application development process, then this book is for you. No previous experience in application testing is required.

This book is ideal for developers who have experience in developing websites or possess minor knowledge of how responsive websites work. No experience of high-level website development or performance tweaking is required.

If you are a developer who is experienced with Cocos2d and Objective-C, and want to take your game development skills to the next level, this book is going to help you achieve your goal.

This book is targeted at system engineers and system administrators who want to upgrade their knowledge and skills in high availability and want to learn practically how to achieve high availability with CentOS Linux. You are expected to have good CentOS Linux knowledge and basic networking experience.

This book is intended for PHP web developers who have an interest in Laravel and who know the basics of the framework in theory, but don't really know how to use it in practice. No experience of using frameworks is required, but it is assumed you are at least familiar with building dynamic websites in PHP already.

If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.

A stepbystep tutorial that will teach you, through realworld examples, how to configure and use zsh and its various features. If you are a system administrator, developer, or computer professional involved with UNIX who are looking to improve on their daily tasks involving the UNIX shell, "Learning Shell Scripting with zsh" will be great for you. It’s assumed that you have some familiarity with an UNIX commandline interface and feel comfortable with editors such as Emacs or vi.

Build, process and analyze large-scale graph data effectively with Spark About This Book Find solutions for every stage of data processing from loading and transforming graph data to Improve the scalability of your graphs with a variety of real-world applications with complete Scala code. A concise guide to processing large-scale networks with Apache Spark. Who This Book Is For This book is for data scientists and big data developers who want to learn the processing and analyzing graph datasets at scale. Basic programming experience with Scala is assumed. Basic knowledge of Spark is assumed. What You Will Learn Write, build and deploy Spark applications with the Scala Build Tool. Build and analyze large-scale network datasets Analyze and transform graphs using RDD and graph-specific operations Implement new custom graph operations tailored to specific needs. Develop iterative and efficient graph algorithms using message aggregation and Pregel abstraction Extract subgraphs and use it to discover common clusters Analyze graph data and solve various data science problems using real-world datasets. In Detail Apache Spark is the next standard of open-source cluster-computing engine for processing big data. Many practical computing problems concern large graphs, like the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. Apache Spark GraphX API combines the advantages of both data-parallel and graph-parallel systems by efficiently expressing graph computation within the Spark data-parallel framework. This book will teach the user to do graphical programming in Apache Spark, apart from an explanation of the entire process of graphical data analysis. You will journey through the creation of graphs, its uses, its exploration and analysis and finally will also cover the conversion of graph elements into graph structures. This book begins with an introduction of the Spark system, its libraries and the Scala Build Tool. Using a hands-on approach, this book will quickly teach you how to install and leverage Spark interactively on the command line and in a standalone Scala program. Then, it presents all the methods for building Spark graphs using illustrative network datasets. Next, it will walk you through the process of exploring, visualizing and analyzing different network characteristics. This book will also teach you how to transform raw datasets into a usable form. In addition, you will learn powerful operations that can be used to transform graph elements and graph structures. Furthermore, this book also teaches how to create custom graph operations that are tailored for specific needs with efficiency in mind. The later chapters of this book cover more advanced topics such as clustering graphs, implementing graph-parallel iterative algorithms and learning methods from graph data. Style and approach A step-by-step guide that will walk you through the key ideas and techniques for processing big graph data at scale, with practical examples that will ensure an overall understanding of the concepts of Spark.

Unleash the power of computer vision with Python using OpenCV About This Book Create impressive applications with OpenCV and Python Familiarize yourself with advanced machine learning concepts Harness the power of computer vision with this easy-to-follow guide Who This Book Is For Intended for novices to the world of OpenCV and computer vision, as well as OpenCV veterans that want to learn about what's new in OpenCV 3, this book is useful as a reference for experts and a training manual for beginners, or for anybody who wants to familiarize themselves with the concepts of object classification and detection in simple and understandable terms. Basic knowledge about Python and programming concepts is required, although the book has an easy learning curve both from a theoretical and coding point of view. What You Will Learn Install and familiarize yourself with OpenCV 3's Python API Grasp the basics of image processing and video analysis Identify and recognize objects in images and videos Detect and recognize faces using OpenCV Train and use your own object classifiers Learn about machine learning concepts in a computer vision context Work with artificial neural networks using OpenCV Develop your own computer vision real-life application In Detail OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance. Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application. Style and approach This book is a comprehensive guide to the brand new OpenCV 3 with Python to develop real-life computer vision applications.

Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R’s advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R’s range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.

Extend the capabilities of AngularJS and build dynamic web applications by creating customized directives with this selection of more than 30 recipes About This Book Learn how to extend HTML templates in new ways to build even better web applications with exceptional interface components Build reusable directives for large-scale AngularJS applications Create even sophisticated and impressive modern web apps with ease Who This Book Is For This book is for developers with AngularJS experience who want to extend their knowledge to create or customize directives in any type of AngularJS application. Some experience of modern tools such as Yeoman and Bower would be helpful, but is not a requirement. What You Will Learn Build and customize external HTML templates, and create simple, effective directives for common interface components Learn how to use Controller function and any Bootstrap UI directives to manipulate the DOM and how to transform any UI library into AngularJS directives Construct an AngularJS application to use shared components and validate your HTML5 Discover how to use jQuery events and manipulate the DOM using jQuery UI inside AngularJS applications Create custom directives for ongoing projects using Yeoman generators, and find out how to implement standalone directives Build reusable directives for Large AngularJS applications and extend directives to use dynamic templates Write unit test for directives using the Karma runner and Jasmine’s behavior-driven development framework In Detail AngularJS directives are at the center of what makes it such an exciting – and important - web development framework. With directives, you can take greater control over HTML elements on your web pages – they ‘direct’ Angular’s HTML compiler to behave in the way you want it to. It makes building modern web applications a much more expressive experience, and allows you to focus more closely on improving the way that user interaction impacts the DOM and the way your app manages data. If you’re already using Angular, you probably recognize the power of directives to transform the way you understand and build your projects – but customizing and creating your own directives to harness AngularJS to its full potential can be more challenging. This cookbook shows you how to do just that – it’s a valuable resource that demonstrates how to use directives at every stage in the workflow. Packed with an extensive range of solutions and tips that AngularJS developers shouldn’t do without, you’ll find out how to make the most of directives. You’ll find recipes demonstrating how to build a number of different user interface components with directives, so you can take complete control over how users interact with your application. You’ll also learn how directives can simplify the way you work by creating reusable directives – by customizing them with Yeoman you can be confident that you’re application has the robust architecture that forms the bedrock of the best user experiences. You’ll also find recipes that will help you learn how to unit test directives, so you can be confident in the reliability and performance of your application. Whether you’re looking for guidance to dive deeper into AngularJS directives, or you want a reliable resource, relevant to today’s web development challenges, AngularJS Directives Cookbook delivers everything you need in an easily accessible way. Style and approach This book easy-to-follow guide is packed with hands-on recipes to help you build modular AngularJS applications with custom directives. It presents tips on using the best tools and various ways to use these tools for front-end development.

Over 100 recipes to help you develop cross-platform, native applications in JavaScript About This Book Leverage your JavaScript skills to write mobile applications using Titanium Studio tools with the native advantage Deploy your application on the App Store and Google Play Add your own IOS native modules in objective-C, in an easy-to-follow step-by-step format Who This Book Is For This book is an essential for any developer learning or using JavaScript who wants to write native UI applications for iOS and Android. No knowledge of Objective-C, Swift and Java is required and you’ll quickly be developing native, cross-platform apps, in JavaScript! What You Will Learn Transfer data between applications with URL schemes, and make your application accessible to other mobile applications and services Connect with remote services using JSON Work with Google Maps and Apple Maps, GPS and annotate routes Create animations and special effects Integrate notifications and connect with social media services such as Facebook and Twitter Build applications with Alloy MVC – a rapid application development framework Design native APIs and use local databases In Detail The mobile web has paved the way but many users want to have “native” applications installed. Using Appcelerator as a platform it’s now possible to write iOS, Android, and Windows phone applications in JavaScript! It allows developers to develop fully native UI applications using Appcelerator studio tools without any knowledge of Objective-C, Swift or Java. This book will take you through the process of building cross-platform, native UI applications for the mobile from scratch. You will learn how to develop apps, how to use GPS, cameras and photos and how to build socially connected apps. You will also learn how to package them for submission to the App Store and Google Play. This cookbook takes a pragmatic approach to creating applications in JavaScript from putting together basic UIs, to handling events and implementation of third party services such as Twitter, Facebook and Push notifications. The book shows you how to integrate datasources and server APIs, and how to use local databases. The topics covered will guide you to use Appcelerator Studio tools for all the mobile features such as Geolocation, Accelerometer, animation and more. You’ll also learn about Alloy, the Appcelerator MVC framework for rapid app development, and how to transfer data between applications using URLSchemes, enabling other developers to access and launch specific parts of your app. Finally, you will learn how to register developer accounts and publish your very own applications on the App Store and Google Play. Style and approach This book offers a set of practical recipes with a step-by-step approach for building native applications for both the iOS and Android using JavaScript. This hands-on guide shows you exactly how to use the Appcelerator platform to rapidly develop cross-platform, native apps.

Control your machine learning algorithms using test-driven development to achieve quantifiable milestones About This Book Build smart extensions to pre-existing features at work that can help maximize their value Quantify your models to drive real improvement Take your knowledge of basic concepts, such as linear regression and Na?ve Bayes classification, to the next level and productionalize their models Play what-if games with your models and techniques by following the test-driven exploration process Who This Book Is For This book is intended for data technologists (scientists, analysts, or developers) with previous machine learning experience who are also comfortable reading code in Python. You may be starting, or have already started, a machine learning project at work and are looking for a way to deliver results quickly to enable rapid iteration and improvement. Those looking for examples of how to isolate issues in models and improve them will find ideas in this book to move forward. What You Will Learn Get started with an introduction to test-driven development and familiarize yourself with how to apply these concepts to machine learning Build and test a neural network deterministically, and learn to look for niche cases that cause odd model behaviour Learn to use the multi-armed bandit algorithm to make optimal choices in the face of an enormous amount of uncertainty Generate complex and simple random data to create a wide variety of test cases that can be codified into tests Develop models iteratively, even when using a third-party library Quantify model quality to enable collaboration and rapid iteration Adopt simpler approaches to common machine learning algorithms Take behaviour-driven development principles to articulate test intent In Detail Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences. Machine learning is applicable to a lot of what you do every day. As a result, you can’t take forever to deliver your first iteration of software. Learning to build machine learning algorithms within a controlled test framework will speed up your time to deliver, quantify quality expectations with your clients, and enable rapid iteration and collaboration. This book will show you how to quantifiably test machine learning algorithms. The very different, foundational approach of this book starts every example algorithm with the simplest thing that could possibly work. With this approach, seasoned veterans will find simpler approaches to beginning a machine learning algorithm. You will learn how to iterate on these algorithms to enable rapid delivery and improve performance expectations. The book begins with an introduction to test driving machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression. You will discover how to test different approaches to na?ve bayes and compare them quantitatively, along with how to apply OOP (Object-Oriented Programming) and OOP patterns to test-driven code, leveraging SciKit-Learn. Finally, you will walk through the development of an algorithm which maximizes the expected value of profit for a marketing campaign by combining one of the classifiers covered with the multiple regression example in the book. Style and approach An example-driven guide that builds a deeper knowledge and understanding of iterative machine learning development, test by test. Each topic develops solutions using failing tests to illustrate problems; these are followed by steps to pass the tests, simply and straightforwardly. Topics which use generated data explore how the data was generated, alongside explanations of the assumptions behind different machine learning techniques.

A guide to everything an Oracle SOA Suite 12c administrator needs to hit the ground running About This Book Understand core administrative tasks such as deployments, purging, startup and shutdown, configuration, and backup and recovery Manage, monitor, and troubleshoot SOA composites and OSB services Follow step-by-step instructions to easily and quickly install a highly available two-node cluster Who This Book Is For With topic areas ranging from the simple to the complex, this book is intended for novice, mid-level, and experienced administrators of the Oracle SOA Suite 12c platform as well as Oracle WebLogic Server and Oracle Database administrators interested in diving into the product. What You Will Learn Navigate Oracle Enterprise Manager Fusion Middleware Control Monitor and manage the Oracle SOA Suite 12 c infrastructure Deploy and promote code Monitor and manage services Configure and administer the environment Manage the dehydration store and enterprise scheduler service Troubleshoot Oracle SOA Suite 12c infrastructure Set up backups, recovery, and high availability In Detail Oracle SOA Suite 12 c is the most comprehensive and integrated infrastructure on the market today that is used for building applications based on service-oriented architecture. With the vast number of features and capabilities that Oracle SOA Suite 12c has to offer comes numerous complexities and challenges for administration. Oracle SOA Suite 12c Administrator's Guide covers all the core areas of administration needed for you to effectively manage and monitor the Oracle SOA Suite environment and its transactions, from deployments, to monitoring, to performance tuning, and much, much more. Manage, monitor, and troubleshoot SOA composites and OSB services from a single product set. Understand core administrative activities such as deployments, purging, startup and shutdown, configuration, backup, and recovery. Also learn about new features such as Oracle Enterprise Scheduler, lazy loading, work manager groups, high availability, and more. Style and approach Presented in a reference guide format where chapters can be read in any sequence, this book explains the core concepts while providing real-world implementation specifics, detailing the what, why, and how of all the administration-related activities that involve Oracle SOA Suite 12c. We take a step-by-step approach and offers tips, instructions, and examples that you can easily follow and execute.

Master the fundamentals of malware analysis for the Windows platform and enhance your anti-malware skill set About This Book Set the baseline towards performing malware analysis on the Windows platform and how to use the tools required to deal with malware Understand how to decipher x86 assembly code from source code inside your favourite development environment A step-by-step based guide that reveals malware analysis from an industry insider and demystifies the process Who This Book Is For This book is best for someone who has prior experience with reverse engineering Windows executables and wants to specialize in malware analysis. The book presents the malware analysis thought process using a show-and-tell approach, and the examples included will give any analyst confidence in how to approach this task on their own the next time around. What You Will Learn Use the positional number system for clear conception of Boolean algebra, that applies to malware research purposes Get introduced to static and dynamic analysis methodologies and build your own malware lab Analyse destructive malware samples from the real world (ITW) from fingerprinting and static/dynamic analysis to the final debrief Understand different modes of linking and how to compile your own libraries from assembly code and integrate the codein your final program Get to know about the various emulators, debuggers and their features, and sandboxes and set them up effectively depending on the required scenario Deal with other malware vectors such as pdf and MS-Office based malware as well as *s and shellcode In Detail Windows OS is the most used operating system in the world and hence is targeted by malware writers. There are strong ramifications if things go awry. Things will go wrong if they can, and hence we see a salvo of attacks that have continued to disrupt the normal scheme of things in our day to day lives. This book will guide you on how to use essential tools such as debuggers, disassemblers, and sandboxes to dissect malware samples. It will expose your innards and then build a report of their indicators of compromise along with detection rule sets that will enable you to help contain the outbreak when faced with such a situation. We will start with the basics of computing fundamentals such as number systems and Boolean algebra. Further, you'll learn about x86 assembly programming and its integration with high level languages such as C++.You'll understand how to decipher disassembly code obtained from the compiled source code and map it back to its original design goals. By delving into end to end analysis with real-world malware samples to solidify your understanding, you'll sharpen your technique of handling destructive malware binaries and vector mechanisms. You will also be encouraged to consider analysis lab safety measures so that there is no infection in the process. Finally, we'll have a rounded tour of various emulations, sandboxing, and debugging options so that you know what is at your disposal when you need a specific kind of weapon in order to nullify the malware. Style and approach An easy to follow, hands-on guide with de*ions and screenshots that will help you execute effective malicious software investigations and conjure up solutions creatively and confidently.

Statistics, big data, and machine learning for Clojure programmers About This Book Write code using Clojure to harness the power of your data Discover the libraries and frameworks that will help you succeed A practical guide to understanding how the Clojure programming language can be used to derive insights from data Who This Book Is For This book is aimed at developers who are already productive in Clojure but who are overwhelmed by the breadth and depth of understanding required to be effective in the field of data science. Whether you’re tasked with delivering a specific analytics project or simply suspect that you could be deriving more value from your data, this book will inspire you with the opportunities–and inform you of the risks–that exist in data of all shapes and sizes. What You Will Learn Perform hypothesis testing and understand feature selection and statistical significance to interpret your results with confidence Implement the core machine learning techniques of regression, classification, clustering and recommendation Understand the importance of the value of simple statistics and distributions in exploratory data analysis Scale algorithms to web-sized datasets efficiently using distributed programming models on Hadoop and Spark Apply suitable analytic approaches for text, graph, and time series data Interpret the terminology that you will encounter in technical papers Import libraries from other JVM languages such as Java and Scala Communicate your findings clearly and convincingly to nontechnical colleagues In Detail The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a *ing language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs. Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility! You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models. Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future. Style and approach This is a practical guide to data science that teaches theory by example through the libraries and frameworks accessible from the Clojure programming language.

Use PhoneGap to build cross-platform mobile applications quickly and efficiently About This Book Build native mobile phone applications with HTML5, JavaScript, and CSS Incorporate smartphone capabilities such as GPS, camera, accelerometer, and more into your apps for any mobile platform Use Cordova view to embed PhoneGap into native applications to either transit smoothly to PhoneGap or incorporate PhoneGap functionalities Who This Book Is For If you are a mobile application developer in iOS or Android, or a web application developer who wants to learn how to make cross-platform mobile applications using PhoneGap, this book is perfect for you. To make the most of this book, it will be helpful if you have prior knowledge of HTML5, CSS, and JavaScript. What You Will Learn Get to grips with the fundamentals of PhoneGap to get started Set up a development environment for Linux, Mac OS, and Windows Use Cordova CLI, workflows, and Plugman Plugin manager to create mobile applications efficiently Understand the development workflow to create native cross-platform mobile applications Embed plugin support to transition to PhoneGap or use it to enhance existing applications Improve your mobile development knowledge using object-oriented programming (OOP), reusable components, and AJAX closures Be empowered to build your own mobile apps quickly with ease Discover tips and tricks to make app development fun and easy In Detail PhoneGap is an open source framework that allows you to quickly build cross-platform mobile apps using HTML5, JavaScript, and CSS. PhoneGap Build is a cloud service that allows you to quickly develop and compile mobile applications without SDKs, compilers, and hardware. PhoneGap allows you to use its existing plugins or create new ones, as per your requirements, to enhance your mobile applications. Starting by installing PhoneGap, you’ll develop an app that uses various device capabilities through different plugins and learn how to build an app in the cloud with PhoneGap’s Build service. You’ll discover how to use PhoneGap to create an application view, along with how to use a camera, geolocation, and other device capabilities to create engaging apps. Next, you’ll augment applications with PhoneGap's plugins using minimalistic code. You’ll explore the app preparation process to deploy your app to the app store. By the end of the book, you’ll have also learned how to apply hybrid mobile UIs that will work across different platforms and different screen sizes for better user experience. Style and approach This is an example-based, fast-paced guide that covers the fundamentals of creating cross-platform mobile applications with PhoneGap.

Harness the power of Redis to integrate and manage your projects efficiently About This Book Learn how to use Redis's data types efficiently to manage large data sets Scale Redis to multiple servers with Twemproxy, Redis Sentinel, and Redis Cluster A fast-paced guide, full of real-world examples to help you get the best out of the features offered by Redis Who This Book Is For If you are a competent developer with experience of working with data structure servers and want to boost your project's performance by learning about features of Redis, then this book is for you. What You Will Learn Build analytics applications using Bitmaps and Hyperloglogs Enhance scalability with Twemproxy, Redis Sentinel, and Redis Cluster Build a Time Series implementation in Node.js and Redis Create your own Redis commands by extending Redis with Lua Get to know security techniques to protect your data (SSL encryption, firewall rules, basic authorization) Persist data to disk and learn the trade-offs of AOF and RDB Understand how to use Node.js, PHP, Python, and Ruby clients for Redis Avoid common pitfalls when designing your next solution In Detail Redis is the most popular in-memory key-value data store. It’s very lightweight and its data types give it an edge over the other competitors. If you need an in-memory database or a high-performance cache system that is simple to use and highly scalable, Redis is what you need. Redis Essentials is a fast-paced guide that teaches the fundamentals on data types, explains how to manage data through commands, and shares experiences from big players in the industry. We start off by explaining the basics of Redis followed by the various data types such as Strings, hashes, lists, and more. Next, Common pitfalls for various scenarios are described, followed by solutions to ensure you do not fall into common traps. After this, major differences between client implementations in PHP, Python, and Ruby are presented. Next, you will learn how to extend Redis with Lua, get to know security techniques such as basic authorization, firewall rules, and SSL encryption, and discover how to use Twemproxy, Redis Sentinel, and Redis Cluster to scale infrastructures horizontally. At the end of this book, you will be able to utilize all the essential features of Redis to optimize your project's performance. Style and approach A practical guide that offers the foundation upon which you can begin to understand the capabilities of Redis using a step-by-step approach. This book is full of real-world problems and in-depth knowledge of the concepts and features of Redis, with plenty of examples.

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python’s most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For 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. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.