Whether you are a hobbyist or a professional, this book will get you fully equipped to resolve the most commonly occurring media-related challenges. If you want to expand your horizons beyond lighting an LED and push the limits of your board, this is just the book for you. Working knowledge of BeagleBone is assumed.

This book is for users experienced with game development who now want to learn how to develop games in GameMaker: Studio in a fast-paced way.

If you are a programmer, visual artist, or designer with experience in creative coding, and want to use openFrameworks to create fun, stunning, and interactive applications, this is the book for you. Basic knowledge of programming languages, such as C++, Java, Python, or JavaScript, will be enough to proceed with the book.

If you are a .NET developer who wants to eliminate the problems related to defective third-party web service integration or batch job failures, then this is the book for you. It is also perfect for those of you who are new to NServiceBus and service-oriented architecture and would like to learn how you can streamline all of your development efforts.

If you know JavaScript and want to start creating 3D graphics that run in any browser, this book is a great choice for you. You don't need to know anything about math or WebGL; all that you need is general knowledge of JavaScript and HTML.

If you are an experienced JavaScript developer who wants to improve the design of his or her applications, or find yourself in a situation to implement an application in an unfamiliar domain, this book is for you. Prior knowledge of JavaScript is required and prior experience with Node.js will also be helpful.

This book is intended for developers who need to quickly become productive with Odoo. You are expected to have experience developing business applications, as well as an understanding of MVC application design and knowledge of the Python programming language.

This book is intended for those who want to learn about electronics and coding by building amazing devices and gadgets with Arduino. If you are an experienced developer who understands the basics of electronics, then you can quickly learn how to build smart devices using Arduino. The only experience needed is a desire to learn about electronics, circuit breadboarding, and coding.

If you have some experience with Zabbix and wish to take your infrastructure to the next level, then this book is for you. Before you start with Zabbix, or monitoring in general, it is best to have some basic Linux knowledge and a good understanding of snmp, virtualization, and *ing.

If you work on Java projects, use Gradle as a build automation tool, and you use dependencies in your project, this is the book for you. Additionally, if you want to deploy your project artifacts as dependencies for other developers using Gradle, you've found the right book.

If you are a beginner to intermediate Puppet engineer looking for guidance to help fix problems with your Puppet deployments, this book is for you.

If you are an experienced system administrator and know how to manage servers and set up web environments but now want to explore Moodle, this book is perfect for you. You'll get to grips with the basics and learn to manage Moodle quickly, focusing on essential tasks. Having prior knowledge of virtual learning environments would be beneficial, but is not mandatory to make the most of this book.

If you are a developer who has knowledge of Ext JS but would like to expand it to encompass the bigger picture of application development, then this book is ideal for you.

Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning About This Book ? Comprehensive coverage of key topics in machine learning with an emphasis on both the theoretical and practical aspects ? More than 15 open source Java tools in a wide range of techniques, with code and practical usage. ? More than 10 real-world case studies in machine learning highlighting techniques ranging from data ingestion up to analyzing the results of experiments, all preparing the user for the practical, real-world use of tools and data analysis. Who This Book Is For This book will appeal to anyone with a serious interest in topics in Data Science or those already working in related areas: ideally, intermediate-level data analysts and data scientists with experience in Java. Preferably, you will have experience with the fundamentals of machine learning and now have a desire to explore the area further, are up to grappling with the mathematical complexities of its algorithms, and you wish to learn the complete ins and outs of practical machine learning. What You Will Learn ? Master key Java machine learning libraries, and what kind of problem each can solve, with theory and practical guidance. ? Explore powerful techniques in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining. ? Apply machine learning to real-world data with methodologies, processes, applications, and analysis. ? Techniques and experiments developed around the latest specializations in machine learning, such as deep learning, stream data mining, and active and semi-supervised learning. ? Build high-performing, real-time, adaptive predictive models for batch- and stream-based big data learning using the latest tools and methodologies. ? Get a deeper understanding of technologies leading towards a more powerful AI applicable in various domains such as Security, Financial Crime, Internet of Things, social networking, and so on. In Detail Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain. Style and approach A practical guide to help you explore machine learning—and an array of Java-based tools and frameworks—with the help of practical examples and real-world use cases.

Build scalable, reliable, and cost-effective applications with a serverless architecture About This Book ? Design a real-world serverless application from scratch ? Learn about AWS Lambda function and how to use Lambda functions to glue other AWS Services ? Use the Java programming language and well-known design patterns. Although Java is used for the examples in this book, the concept is applicable across all languages ? Learn to migrate your JAX-RS application to AWS Lambda and API Gateway Who This Book Is For This book is for developers and software architects who are interested in designing on the back end. Since the book uses Java to teach concepts, knowledge of Java is required. What You Will Learn ? Learn to form microservices from bigger Softwares ? Orchestrate and scale microservices ? Design and set up the data flow between cloud services and custom business logic ? Get to grips with cloud provider’s APIs, limitations, and known issues ? Migrate existing Java applications to a serverless architecture ? Acquire deployment strategies ? Build a highly available and scalable data persistence layer ? Unravel cost optimization techniques In Detail Over the past years, all kind of companies from start-ups to giant enterprises started their move to public cloud providers in order to save their costs and reduce the operation effort needed to keep their shops open. Now it is even possible to craft a complex software system consisting of many independent micro-functions that will run only when they are needed without needing to maintain individual servers. The focus of this book is to design serverless architectures, and weigh the advantages and disadvantages of this approach, along with decision factors to consider. You will learn how to design a serverless application, get to know that key points of services that serverless applications are based on, and known issues and solutions. The book addresses key challenges such as how to slice out the core functionality of the software to be distributed in different cloud services and cloud functions. It covers basic and advanced usage of these services, testing and securing the serverless software, automating deployment, and more. By the end of the book, you will be equipped with knowledge of new tools and techniques to keep up with this evolution in the IT industry. Style and approach The book takes a pragmatic approach, showing you all the examples you need to build efficient serverless applications.

A practical guide to help you undertand the ability of Apache jMeter to load and performance test various server types in a more efficient way. About This Book ? Use jMeter to create and run tests to improve the performance of your webpages and applications ? Learn to build a test plan for your websites and analyze the results ? Unleash the power of various features and changes introduced in Apache jMeter 3.0 Who This Book Is For This book is for software professionals who want to understand and improve the performance of their applications with Apache jMeter. What You Will Learn ? See why performance testing is necessary and learn how to set up JMeter ? Record and test with JMeter ? Handle various form inputs in JMeter and parse results during testing ? Manage user sessions in web applications in the context of a JMeter test ? Monitor JMeter results in real time ? Perform distributed testing with JMeter ? Get acquainted with helpful tips and best practices for working with JMeter In Detail JMeter is a Java application designed to load and test performance for web application. JMeter extends to improve the functioning of various other static and dynamic resources. This book is a great starting point to learn about JMeter. It covers the new features introduced with JMeter 3 and enables you to dive deep into the new techniques needed for measuring your website performance. The book starts with the basics of performance testing and guides you through recording your first test scenario, before diving deeper into JMeter. You will also learn how to configure JMeter and browsers to help record test plans. Moving on, you will learn how to capture form submission in JMeter, dive into managing sessions with JMeter and see how to leverage some of the components provided by JMeter to handle web application HTTP sessions. You will also learn how JMeter can help monitor tests in real-time. Further, you will go in depth into distributed testing and see how to leverage the capabilities of JMeter to accomplish this. You will get acquainted with some tips and best practices with regard to performance testing. By the end of the book, you will have learned how to take full advantage of the real power behind Apache JMeter. Style and approach The book is a practical guide starting with introducing the readers to the importance of automated testing. It will then be a beginner’s journey from getting introduced to Apache jMeter to an in-detail discussion of more advanced features and possibilities with it.

Build Machine Learning models with a sound statistical understanding. About This Book ? Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. ? Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. ? Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn ? Understand the Statistical and Machine Learning fundamentals necessary to build models ? Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems ? Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages ? Analyze the results and tune the model appropriately to your own predictive goals ? Understand the concepts of required statistics for Machine Learning ? Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models ? Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.

Use scikit-learn to apply machine learning to real-world problems About This Book ? Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks ? Learn how to build and evaluate performance of efficient models using scikit-learn ? Practical guide to master your basics and learn from real life applications of machine learning Who This Book Is For This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required. What You Will Learn ? Review fundamental concepts such as bias and variance ? Extract features from categorical variables, text, and images ? Predict the values of continuous variables using linear regression and K Nearest Neighbors ? Classify documents and images using logistic regression and support vector machines ? Create ensembles of estimators using bagging and boosting techniques ? Discover hidden structures in data using K-Means clustering ? Evaluate the performance of machine learning systems in common tasks In Detail Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. Style and approach This book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and models, then apply your learnings to real-life problems.

Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book ? Learn Scala’s sophisticated type system that combines Functional Programming and object-oriented concepts ? Work on a wide array of applications, from simple batch jobs to stream processing and machine learning ? Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn ? Understand object-oriented & functional programming concepts of Scala ? In-depth understanding of Scala collection APIs ? Work with RDD and DataFrame to learn Spark’s core abstractions ? Analysing structured and unstructured data using SparkSQL and GraphX ? Scalable and fault-tolerant streaming application development using Spark structured streaming ? Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML ? Build clustering models to cluster a vast amount of data ? Understand tuning, debugging, and monitoring Spark applications ? Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big. Style and approach Filled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist.

Advanced analytics on your Big Data with latest Apache Spark 2.x About This Book ? An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities. ? Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark. ? Master the art of real-time processing with the help of Apache Spark 2.x Who This Book Is For If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected. What You Will Learn ? Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J ? Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming ? Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames ? Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud ? Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames ? Learn how specific parameter settings affect overall performance of an Apache Spark cluster ? Leverage Scala, R and python for your data science projects In Detail Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark’s functionality and implement your data flows and machine/deep learning programs on top of the platform. The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x. You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets. You will also learn about the updates on the APIs and how DataFrames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks. Style and approach This book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.

Learn the art of leveraging PowerShell to automate Office 365 repetitive tasks About This Book ? Master the fundamentals of PowerShell to automate Office 365 tasks. ? Easily administer scenarios such as user management, reporting, cloud services, and many more. ? A fast-paced guide that leverages PowerShell commands to increase your productivity. Who This Book Is For The book is aimed at sys admins who are administering office 365 tasks and looking forward to automate the manual tasks. They have no knowledge about PowerShell however basic understanding of PowerShell would be advantageous. What You Will Learn ? Understand the benefits of *ing and automation and get started using Powershell with Office 365 ? Explore various PowerShell packages and permissions required to manage Office 365 through PowerShell ? Create, manage, and remove Office 365 accounts and licenses using PowerShell and the Azure AD ? Learn about using powershell on other platforms and how to use Office 365 APIs through remoting ? Work with Exchange Online and SharePoint Online using PowerShell ? Automate your tasks and build easy-to-read reports using PowerShell In Detail While most common administrative tasks are available via the Office 365 admin center, many IT professionals are unaware of the real power that is available to them below the surface. This book aims to educate readers on how learning PowerShell for Office 365 can simplify repetitive and complex administrative tasks, and enable greater control than is available on the surface. The book starts by teaching readers how to access Office 365 through PowerShell and then explains the PowerShell fundamentals required for automating Office 365 tasks. You will then walk through common administrative cmdlets to manage accounts, licensing, and other scenarios such as automating the importing of multiple users,assigning licenses in Office 365, distribution groups, passwords, and so on. Using practical examples, you will learn to enhance your current functionality by working with Exchange Online, and SharePoint Online using PowerShell. Finally, the book will help you effectively manage complex and repetitive tasks (such as license and account management) and build productive reports. By the end of the book, you will have automated major repetitive tasks in Office 365 using PowerShell. Style and approach This step by step guide focuses on teaching the fundamentals of working with PowerShell for Office 365. It covers practical usage examples such as managing user accounts, licensing, and administering common Office 365 services. You will be able to leverage the processes laid out in the book so that you can move forward and explore other less common administrative tasks or functions.