Learn end-to-end automation testing techniques for web and mobile browsers using Selenium WebDriver, AppiumDriver, Java, and TestNG Key Features * Explore the Selenium grid architecture and build your own grid for browser and mobile devices * Use ExtentReports for processing results and SauceLabs for cloud-based test services * Unlock the full potential of Selenium to test your web applications. Book Description Selenium WebDriver 3.x is an open source API for testing both browser and mobile applications. With the help of this book, you can build a solid foundation and can easily perform end-to-end testing on web and mobile browsers.You'll begin by being introduced to the Selenium Page Object Model for software development. You'll architect your own framework with a scalable driver class, Java utility classes, and support for third-party tools and plugins. You'll design and build a Selenium grid from scratch to enable the framework to scale and support different browsers, mobile devices, and platforms.You'll strategize and handle a rich web UI using the advanced WebDriver API and learn techniques to handle real-time challenges in WebDriver. You'll perform different types of testing, such as cross-browser testing, load testing, and mobile testing. Finally, you will also be introduced to data-driven testing, using TestNG to create your own automation framework.By the end of this Learning Path, you'll be able to design your own automation testing framework and perform data-driven testing with Selenium WebDriver. This Learning Path includes content from the following Packt products: * Selenium WebDriver 3 Practical Guide - Second Edition by Unmesh Gundecha * Selenium Framework Design in Data-Driven Testing by Carl Cocchiaro What you will learn * Use different mobile and desktop browser platforms with Selenium 3 * Use the Actions API for performing various keyboard and mouse actions * Design the Selenium Driver Class for local, remote, and third-party grid support * Build page object classes with the Selenium Page Object Model * Develop data-driven test classes using the TestNG framework * Encapsulate data using the JSON protocol * Build a Selenium Grid for RemoteWebDriver testing * Build and use utility classes in synchronization, file I/O, reporting and test listener classes Who this book is for This Learning Path is ideal for software quality assurance/testing professionals, software project managers, or software developers interested in using Selenium for testing their applications. Professionals responsible for designing and building enterprise-based testing frameworks will also find this Learning Path useful. Prior programming experience in Java are TestNG is necessary.

Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key Features * Perform efficient data analysis and manipulation tasks using pandas * Apply pandas to different real-world domains using step-by-step demonstrations * Get accustomed to using pandas as an effective data exploration tool Book Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learn * Understand how data analysts and scientists gather and analyze data * Perform data analysis and data wrangling in Python * Combine, group, and aggregate data from multiple sources * Create data visualizations with pandas, matplotlib, and seaborn * Apply machine learning (ML) algorithms to identify patterns and make predictions * Use Python data science libraries to analyze real-world datasets * Use pandas to solve common data representation and analysis problems * Build Python scripts, modules, and packages for reusable analysis code Who this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.

Gain useful insights to help you efficiently build, test, and migrate customized solutions on Business Central cloud and on-premise platforms Key Features * Explore enhanced functionalities and development best practices in Business Central * Develop powerful Business Central projects using the AL language * Master the new Business Central with easy-to-follow recipes Book Description Microsoft Dynamics 365 Business Central is a complete business management solution that can help you streamline business processes, connect individual departments in your company, and enhance customer interactions. Ok. That first part was really professional sounding, right? Now, let’s get into what this cookbook is going to do for you: put simply, it’s going to help you get things done. This book will help you get to grips with the latest development features and tools for building applications using Business Central. You’ll find recipes that will guide you in developing and testing applications that can be deployed to the cloud or on-premises. For the old-schoolers out there, you’ll also learn how to take your existing Dynamics NAV customizations and move them to the new AL language platform. Also, if you haven’t figured it out already, we’re going to be using very normal language throughout the book to keep things light. After all, developing applications is fun, so why not have fun learning as well! What you will learn * Build and deploy Business Central applications * Use the cloud or local sandbox for application development * Customize and extend your base Business Central application * Create external applications that connect to Business Central * Create automated tests and debug your applications * Connect to external web services from Business Central Who this book is for This book is for Dynamics developers and administrators who want to become efficient in developing and deploying applications in Business Central. Basic knowledge and understanding of Dynamics application development and administration is assumed.

☆★☆ Javascript: Javascript Programming For Absolute Beginners☆★☆ The best starter guide for javascript!The fundamentals of javascript are often missed, however, this book's primary focus and emphasis is learning the essentials and to build from the ground up.? What You'lll Learn The history of JavaScript and its uses Setting Up Your Environment The Vital Basics of HTML and CSS Statements, Comments & Variables How to properly use jQuery String Operators JavaScript Array Methods Loops and Iteration How To Use Functions And much, much more! ? Within this book you will learn various mechanisms of javascript programming: variables, conditional statements, and why learning these core principles lead to success.Once you gain knowledge of the fundamental building blocks of javascript you will gain confidence to tackle more complex topics down the road.Programming books can easily retail for 100s of dollars, why not start with an expert you can trust and for an affordable price?The immense value of this book cannot be understated, and this is a once in a life time opportunity for you to take advantage of and invest in yourself by empowering yourself with the right tools and knowledge for success.What are you waiting for?Includes: 21 practice questions! Note* For best visual experience of diagrams it is highly recommend you purchases the paperback version for best image quality.☆★☆ Grab your copy now!☆★☆

Bu kitap “Bili?im Teknolojileri (BT) Ekonomisi ve Toplumu” adl? ilk kitab?mda BT’lerin hayat?m?z? de?i?tirdi?i d?rt yerdeki (Evde, okulda, kentlerde ve i?yerlerinde) incelememin ü?üncüsü olan i?yerlerindeki etkileri üzerinedir. Daha ?nce “Bili?im Teknolojileri Destekli ??renim” ve “Bili?im Kentleri ?a??” adl? kitaplar?mda BT’lerin okul ve kent ya?am?ndaki etkilerini detaylar?yla inceledim. 30 y?l? a?an i? hayat?mda BT’lerin i? dünyas?n? ve y?netimini kurulan Bili?im Sistemleri (BS) vas?tas?yla nas?l de?i?tirdi?ini ya?ayarak bildi?imden bu konu i?in uzun bir ara?t?rma yapmak zorunda oldu?umu biliyordum. Di?er yandan 592 sayfay? bulan bu ara?t?rmada sayfa s?n?rlamas? amac?yla BS’lerle ilgili baz? konular? (?rne?in Toplam Kalite Y?netimi, 6 Sigma, Simülasyon, Gereksinim Y?netimi, Programlama vs.) kitap haricinde b?rakmak zorunda kald???m? da belirtmeliyim. Bu kitapta a??rl?kl? olarak BT’lerin i?letmelerdeki uygulamas? olan Bili?im Sistemlerinden bahsettim ?ünkü BT’ler BS’ler vas?tas?yla i?letmeleri ve y?netimlerini etkilemektedirler. Dünyan?n en h?zl? bilgisayar?n? veya en yeni ak?ll? telefonunu sat?n alman?z veya en h?zl? internet eri?imine sahip olman?z i?letmeye ekstra bir katk? sa?lamamaktad?r. Ne zaman ki bu ara?lar i?letme süre?lerinin otomasyonunu sa?layan BS’ler i?inde kullan?lmaya ba?lan?nca i?letme rekabet?ili?ine, kar?na veya maliyetlerinin kontrolüne bir faydas? olabilmektedir. Bu yüzden i?letme y?neticilerinin bilmesi gereken en ?nemli konu BT ara?lar?n? ve di?er yeni teknolojik ara?lar? i?letme i? süre?lerinde nas?l verimli ve etkin bir ?ekilde kullanabileceklerini bilmeleridir. Bilmiyorlarsa da bilenleri i?e almalar?d?r. En son yenilikleri kullanmak belki de firmaya zarar vermekte veya rekabet dezavantaj? olu?turmaktad?r. Bu a??dan ?ncelikle bugün BS’lerin hangi i?letme fonksiyonlar?n? nas?l etkiledi?ini ve gelecekte nas?l etkileyebilece?ini anlatmaya ?al??t?m. ?rne?in, Yapay Zek? (YZ) ve onun en ?nemli uygulamalar?ndan birisi olan Robotiklerin i? süre?leri ve i?letme y?netimlerini yak?n bir zamanda nas?l etkileyebilece?inden bahsettim. Endüstri 4.0 teknolojileriyle insan ve makinelerin birlikte nas?l verimli ve etkin bir ?ekilde ?al??malar? gerekti?i insanl???n ve i?letmelerin ?nünde duran en ?nemli konulardan birisi oldu?unu s?ylemek fazla fütüristik bir kehanet de?il. Kitapta ??letmelerde kullan?lan Bili?im Sistemleriyle ilgili temel konulara bir bütünlük i?inde bakarken kitab?n arka kapa??ndaki sorular? cevaplamaya ?al??t?m. Bili?im Sistemleri aras?ndaki ili?kileri, farkl?l?klar?n? ve birbirlerini nas?l tamamlad?klar?n? sat?r aralar?nda vermeye ?al??t?m. BS’lerle i?letme süre?lerinin nas?l bütünle?tirilece?i i?letmelerdeki en yeni y?netim sorunlar? oldu?u unutulmamal?d?r. ??letme y?neticilerinin hat?rlamas? gereken bir ?nemli konuda BS’lerin bir yaz?l?m ve de?i?im projesi olmas?d?r. Bili?im Teknolojileri ve Sistemleri okuryazarl??? i?in bilinmesi gereken baz? teknik, bilimsel ve teknolojik terimlerin k?saltmalar?n? kitapta ilk kullan?ld???nda uzun ve k?salt?lm?? yaz?l?mlar?yla birlikte kulland?m. ?rne?in Veritabanlar? (VT), Veri ??leme Sistemi (V?S), Kurumsal ?? Zek?s? (K?Z), Y?netim Bili?im Sistemi (YBS), Karar Destek Sistemi (KDS), Veri Ambar? (VA), Veri Madencili?i (VM), Kurumsal Kaynak Planlama (KKP), Mü?teri ?li?kileri Y?netimi (M?Y), Tedarik Zinciri Y?netimi (TZY), Ofis Otomasyon Sistemi (OOS) ve Bilgi Y?netimi (BY) en fazla kulland???m k?saltmalard?r. Kitap sonundaki “K?saltmalar” tablosunda tüm k?saltmalar? listelemeye ?al??t?m. Ayr?ca bir?ok terimin ?ngilizcesini de parantez i?inde vermeye ?al??t?m. ?rne?in Veritabanlar? (Database) gibi. Dilimize girmi? bir?ok yabanc? teknik terim ve kelimelerin Türk?esini kullanmaya ?zen g?sterdim ve bu yabanc? kelimeleri de parantez i?inde yazd?m. Ayr?ca BT’lerin BS’ler vas?tas?yla i?letmeler üzerindeki etkilerini incelerken, birazda üniversitelerimizde YBS b?lümlerinde okutulan BS’lerle ilgili uzmanl?k konular?n?n ?o?unu ?zetlemeye ?al??t?m. ?zellikle i? Dünyas?nda ?ok?a kullan?lan baz? konulara (V?S, YBS, KDS, VT, VA, BY, US, KKP, M?Y, Sistem yakla??m?, SGYD vs.) biraz detayl? bakmaya ?al??t?m. K?sacas? kitapta i?letmelerde Bili?im Teknolojileri ve Sistemleri denince akla gelebilecek bir?ok konuya de?inmeye ?al??t?m. Bu yüzden bu kitab?n BS alan?nda bir elkitab? veya kaynak kitap olarak dü?ünülmesinde fayda vard?r. Bu kitapta ilkokul ??retmenim Say?n ?erare ?zya?c? han?mdan, en son Doktora tez dan??man?m say?n Prof. Dr. Len Rogers’a kadar yüzlerce ki?inin eme?inin oldu?u unutulmamal?d?r. 1984’ten beri Türkiye, ABD, Kanada ve ?ngiltere de ?al??t???m veya dan??manl???n? yapt???m onlarca firma ve mü?terilerimin katk?lar?n? unutabilir miyim? Burada isim isim te?ekkür edemedi?im ancak bu kitab?n yaz?lmas?nda katk?lar? olan daha yüzlerce ki?i var, hepsine en i?ten dileklerimle te?ekkür ederim. Hayatta neyi tek ba??m?za yapabiliyoruz ki? Bu kitap 30+ y?ld?r üretim planlama ve stok kontrol müdürü, metot etüdcü, sistem analisti, programc?, VT tasar?mc?s?

An ebook is just a website in a boxBut what’s inside the box?In this clear, concise guide, ebook designer and indie author David Kudler folds back the lid of ePub, the universal ebook format. He introduces you to the nuts and bolts that make an ebook work. Includes overviews of the ePub format and internal structure as well as basic guides to the HTML and CSS (Cascading Style Sheets) that you need to know to make your ebooks look professional.(Self-Publishing & Ebook Creation)David Kudler is an independent publisher and author. He has been designing ebooks since 2010. He blogs about ebook creation, publishing, and marketing on Huffington Post, Stillpoint Digital Press, and The Book Designer.

Frustrated with the plethora of conflicting information on how to self-publish? Wouldn’t it be wonderful to sit down with someone who has already made the mistakes, done the analysis, and will provide you the short cuts—the secrets about the things that work? Now you have that chance with the Career Author Secrets series.Indie Publishing (Self Publishing) has changed dramatically in the past five years. There are now new, easier tools to use for every part of the process—editing, formatting, distribution, sales, and analysis. This first book in the Career Author Secrets series provides a foundation for navigating the indie publishing process and staying away from the scammers. It breaks down the requirements for self-publishing successfully, protecting your rights for the future, and YES I do share all the secrets I’ve learned. It contains everything a DIY author needs to get her book from manuscript to professional publication in both ebook and print, including: Why books don’t sell Options for DIY or contracting professionals The truth about ISBNs & Copyright Registration Secrets for formatting your book the easy way Creating book covers that sell Making decisions about distribution This book is especially valuable for those with limited technical skills who want to produce a quality professional book for the least amount of cost. Learn the secrets to easier implementation and how to make good decisions on what is worth your time and money.

“Bili?im Teknolojileri” konusunda yazd???m ü? kitaptan sonra as?l uzmanl?k alan?m olan veritabanlar? konusunda uzun y?llar ?nce yazmaya ba?lay?p bitiremedi?im daha do?rusu bas?lmayan bu kitab? güncelleyerek yeniden yazmaya karar vermek benim a??mdan yeni bir heyecan oldu. 1980’lerde COBOL ile ba?layan ve 1990’da dBase ve Informix’le devam eden veritaban? tecrübelerim, 1995 sonras? MS-Access ve SQL Server yard?m?yla geli?tirdi?im 100’e yak?n veritaban? uygulama programlar?yla devam etti. 1996’da Dallas’ta MCI Systemhouse’da FrontPage ve Access 95’le veritaban?na dayal? Intranet web siteleri geli?tiren ilk ki?ilerden birisi ben oldum. 1999’da Movo Mediya’da ilk ??p?atan web sitesi www.dating.com'un?arkas?ndaki SQL Server veritabanlar?n? tasarlayan ve y?neten ki?i de bendim. 2000 y?l?nda Aris Genesis Intermedia Inc’de web tabanl? muhasebe program?n?n arkas?ndaki SQL Server veritaban? tasar?mc?lar?ndan biriside bendim. Büyük al??veri? merkezlerinde g?rülen Kiosk’lar?n SQL Server tabanl? ilk uygulamas?n? Los Angeles’de kuran Genesis Intermedia Inc’deki tasar?mlar? yapan ki?ilerden birisi de bendim. 2001’de Los Angeles’de SQL Server ve ASP 3.0’la Citibank’ta kredi kartlar?yla ilgili projenin mimarlar?ndan biriside bendim. K?sacas? Veritabanlar?n?n Web’de kullan?lmas?nda ilk ?al??an ve tasarlayanlardan birisi oldu?umu s?ylemeliyim. 1995-2000 y?llar? aras?nda ya?anan ve dot com bom olarak bilinen metaforun i?inde bir fiil ?al??an ve yarat?c?lar?ndan birisiydim. Veritabanlar?n?n Internet’e ba?lanmas? i?in geli?tirilen ilk projelerinde uzun y?llar Kalifornya, New York ve Colorado firmalar?nda ?al??t?m ve dan??manl?k yapt?m. Bu tecrübelerim s?ras?nda ??rendi?im en ?nemli ?ey; tek bir konuda uzman olmak gerekti?idir. Ben Ms-Access ve SQL Server veritabanlar? tasar?m?nda uzmanla?t?m. Bir?ok teklif olmas?na ra?men Oracle, DB2, Aproach, File Maker gibi veritabanlar? tasar?mlar?yla ilgilenmedim ?ünkü her biri ayr? bir uzmanl?k isteyen veritaban? yaz?l?mlar?d?r. Bu a??dan ?zellikle Bili?im sekt?ründe ?al??anlara verebilece?im en ?nemli tavsiye tek bir konuda hatta tek bir programda uzmanla?malar?d?r. Kitapta Veritabanlar? tasar?m?n? Access ve SQL Server projeleri üzerinden anlatmaya ?al??t?m. University of Phoenix’de verdi?im Veritabanlar? ve Veri Ambarlar? ders notlar?m bu kitab?n omurgas?n? olu?turmaktad?r. Veritabanlar?yla ilgili kavramlar?, felsefesini, tasar?m?n? ve birazda y?netimini detaylar?yla anlatmaya ?al??t?m. Umut ediyorum ki yeni Veritaban? tasar?mc?lar? ve y?neticilerine burada payla?t???m enformasyon faydal? olur. 30 y?ll?k Bili?im Teknolojileri ve ?zellikle veritaban? alan?ndaki tecrübelerimin bir?o?unu bu kitapta okuyucularla payla?maya ?al??t?m. Daha ?nceki kitaplar?mda da vurgulad???m gibi bu kitapta da ilkokul ??retmenim Say?n ?erare ?zya?c? han?mdan, en son Doktora tez dan??man?m Say?n Prof. Dr. Len Rogers’a kadar yüzlerce ki?inin eme?inin oldu?u unutulmamal?d?r. 1984’ten beri Türkiye, ABD, Kanada ve ?ngiltere de ?al??t???m veya dan??manl???n? yapt???m onlarca firma ve mü?terilerimin katk?lar?n? unutabilir miyim? Di?er kitaplarda oldu?u gibi bu kitab?n yaz?lmas?nda bana katlanan e?im Meliha ?oruh’a ve o?lum Bu?ra’ya da en ba?tan te?ekkür etmeliyim. Burada isim isim te?ekkür edemedi?im ancak bu kitab?n yaz?lmas?nda katk?lar? olan daha yüzlerce ki?i var, hepsine en i?ten dileklerimle te?ekkür ediyorum. Elinizde tuttu?unuz bu kitap 30 y?ld?r Bili?im Teknolojileri alan?nda yapt???m ??retim, ?al??ma ve tecrübelerin veritabanlar? alan?nda kay?t alt?na al?nm?? bir ?zetidir. Tabii ki kitab?n hatalar? ve eksikleri vard?r ve bunlar tamamen bana aittir. Kitapta Veritabanlar? denince akla gelebilecek bir?ok konuya de?inmeye ?al??t?m. Kitapta teori ile uygulamay? birlikte harmanlamaya ?al??t?m. Bu yüzden bu kitab?n bir elkitab? veya kaynak kitap olarak dü?ünülmesinde fayda vard?r. ? Mustafa ?oruh Kdz. Ere?li, Mart 2017

Apply cloud native patterns and practices to deliver responsive, resilient, elastic, and message-driven systems with confidence Key Features * Discover best practices for applying cloud native patterns to your cloud applications * Explore ways to effectively plan resources and technology stacks for high security and fault tolerance * Gain insight into core architectural principles using real-world examples Book Description Cloud computing has proven to be the most revolutionary IT development since virtualization. Cloud native architectures give you the benefit of more flexibility over legacy systems. This Learning Path teaches you everything you need to know for designing industry-grade cloud applications and efficiently migrating your business to the cloud. It begins by exploring the basic patterns that turn your database inside out to achieve massive scalability. You’ll learn how to develop cloud native architectures using microservices and serverless computing as your design principles. Then, you’ll explore ways to continuously deliver production code by implementing continuous observability in production. In the concluding chapters, you’ll learn about various public cloud architectures ranging from AWS and Azure to the Google Cloud Platform, and understand the future trends and expectations of cloud providers. By the end of this Learning Path, you’ll have learned the techniques to adopt cloud native architectures that meet your business requirements. This Learning Path includes content from the following Packt products: * Cloud Native Development Patterns and Best Practices by John Gilbert * Cloud Native Architectures by Erik Farr et al. What you will learn * Understand the difference between cloud native and traditional architecture * Automate security controls and configuration management * Minimize risk by evolving your monolithic systems into cloud native applications * Explore the aspects of migration, when and why to use it * Apply modern delivery and testing methods to continuously deliver production code * Enable massive scaling by turning your database inside out Who this book is for This Learning Path is designed for developers who want to progress into building cloud native systems and are keen to learn the patterns involved. Software architects, who are keen on designing scalable and highly available cloud native applications, will also find this Learning Path very useful. To easily grasp these concepts, you will need basic knowledge of programming and cloud computing.

Leverage the power of reward-based training for your deep learning models with Python Key Features * Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP) * Study practical deep reinforcement learning using Q-Networks * Explore state-based unsupervised learning for machine learning models Book Description Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you’ll gain a sense of what’s in store for reinforcement learning. By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow. What you will learn * Explore the fundamentals of reinforcement learning and the state-action-reward process * Understand Markov decision processes * Get well versed with libraries such as Keras, and TensorFlow * Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym * Choose and optimize a Q-Network’s learning parameters and fine-tune its performance * Discover real-world applications and use cases of Q-learning Who this book is for If you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.

Explore the exciting world of machine learning with the fastest growing technology in the world Key Features * Understand various machine learning concepts with real-world examples * Implement a supervised machine learning pipeline from data ingestion to validation * Gain insights into how you can use machine learning in everyday life Book Description Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own! What you will learn * Understand the concept of supervised learning and its applications * Implement common supervised learning algorithms using machine learning Python libraries * Validate models using the k-fold technique * Build your models with decision trees to get results effortlessly * Use ensemble modeling techniques to improve the performance of your model * Apply a variety of metrics to compare machine learning models Who this book is for Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.

Explore practical use cases to learn everything from Linux components, and functionalities, through to hardware and software support Key Features * Gain a clear understanding of how to design a Linux environment * Learn more about the architecture of the modern Linux operating system(OS) * Understand infrastructure needs and design a high-performing computing environment Book Description It is very important to understand the flexibility of an infrastructure when designing an efficient environment. In this book, you will cover everything from Linux components and functionalities through to hardware and software support, which will help you to implement and tune effective Linux-based solutions. This book gets started with an overview of Linux design methodology. Next, you will focus on the core concepts of designing a solution. As you progress, you will gain insights into the kinds of decisions you need to make when deploying a high-performance solution using Gluster File System (GlusterFS). In the next set of chapters, the book will guide you through the technique of using Kubernetes as an orchestrator for deploying and managing containerized applications. In addition to this, you will learn how to apply and configure Kubernetes for your NGINX application. You’ll then learn how to implement an ELK stack, which is composed of Elasticsearch, Logstash, and Kibana. In the concluding chapters, you will focus on installing and configuring a Saltstack solution to manage different Linux distributions, and explore a variety of design best practices. By the end of this book, you will be well-versed with designing a high-performing computing environment for complex applications to run on. By the end of the book, you will have delved inside the most detailed technical conditions of designing a solution, and you will have also dissected every aspect in detail in order to implement and tune open source Linux-based solutions What you will learn * Study the basics of infrastructure design and the steps involved * Expand your current design portfolio with Linux-based solutions * Discover open source software-based solutions to optimize your architecture * Understand the role of high availability and fault tolerance in a resilient design * Identify the role of containers and how they improve your continuous integration and continuous deployment pipelines * Gain insights into optimizing and making resilient and highly available designs by applying industry best practices Who this book is for This intermediate-level book is for Linux system administrators, Linux support engineers, DevOps engineers, Linux consultants or any open source technology professional looking to learn or expand their knowledge in architecting, designing and implementing solutions based on Linux and open source software. Prior experience in Linux is required.

Plan and build useful machine learning systems for financial services, with full working Python code Key Features * Build machine learning systems that will be useful across the financial services industry * Discover how machine learning can solve finance industry challenges * Gain the machine learning insights and skills fintech companies value most Book Description Machine learning skills are essential for anybody working in financial data analysis. Machine Learning for Finance shows you how to build machine learning models for use in financial services organizations. It shows you how to work with all the key machine learning models, from simple regression to advanced neural networks. You will see how to use machine learning to automate manual tasks, identify and address systemic bias, and find new insights and patterns hidden in available data. Machine Learning for Finance encourages and equips you to find new ways to use data to serve an organization’s business goals. Broad in scope yet deeply practical in approach, Machine Learning for Finance will help you to apply machine learning in all parts of a financial organization’s infrastructure. If you work or plan to work in fintech, and want to gain one of the most valuable skills in the sector today, this book is for you. What you will learn * Practical machine learning for the finance sector * Build machine learning systems that support the goals of financial organizations * Think creatively about problems and how machine learning can solve them * Identify and reduce sources of bias from machine learning models * Apply machine learning to structured data, natural language, photographs, and written text related to finance * Use machine learning to detect fraud, forecast financial trends, analyze customer sentiments, and more * Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow Who this book is for Machine Learning for Finance is for financial professionals who want to develop and apply machine learning skills, and for students entering the field. You should be comfortable with Python and the basic data science stack, such as NumPy, pandas, and Matplotlib, to get the most out of this book.

This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering Key Features * Your handy guide to building machine learning workflows in Go for real-world scenarios * Build predictive models using the popular supervised and unsupervised machine learning techniques * Learn all about deployment strategies and take your ML application from prototype to production ready Book Description Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go. The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced. The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum. The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring. At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones. What you will learn * Understand the types of problem that machine learning solves, and the various approaches * Import, pre-process, and explore data with Go to make it ready for machine learning algorithms * Visualize data with gonum/plot and Gophernotes * Diagnose common machine learning problems, such as overfitting and underfitting * Implement supervised and unsupervised learning algorithms using Go libraries * Build a simple web service around a model and use it to make predictions Who this book is for This book is for developers and data scientists with at least beginner-level knowledge of Go, and a vague idea of what types of problem Machine Learning aims to tackle. No advanced knowledge of Go (and no theoretical understanding of the math that underpins Machine Learning) is required.

Explore Golang's data structures and algorithms to design, implement, and analyze code in the professional setting Key Features * Learn the basics of data structures and algorithms and implement them efficiently * Use data structures such as arrays, stacks, trees, lists and graphs in real-world scenarios * Compare the complexity of different algorithms and data structures for improved code performance Book Description Golang is one of the fastest growing programming languages in the software industry. Its speed, simplicity, and reliability make it the perfect choice for building robust applications. This brings the need to have a solid foundation in data structures and algorithms with Go so as to build scalable applications. Complete with hands-on tutorials, this book will guide you in using the best data structures and algorithms for problem solving. The book begins with an introduction to Go data structures and algorithms. You'll learn how to store data using linked lists, arrays, stacks, and queues. Moving ahead, you'll discover how to implement sorting and searching algorithms, followed by binary search trees. This book will also help you improve the performance of your applications by stringing data types and implementing hash structures in algorithm design. Finally, you'll be able to apply traditional data structures to solve real-world problems. By the end of the book, you'll have become adept at implementing classic data structures and algorithms in Go, propelling you to become a confident Go programmer. What you will learn * Improve application performance using the most suitable data structure and algorithm * Explore the wide range of classic algorithms such as recursion and hashing algorithms * Work with algorithms such as garbage collection for efficient memory management * Analyze the cost and benefit trade-off to identify algorithms and data structures for problem solving * Explore techniques for writing pseudocode algorithm and ace whiteboard coding in interviews * Discover the pitfalls in selecting data structures and algorithms by predicting their speed and efficiency Who this book is for This book is for developers who want to understand how to select the best data structures and algorithms that will help solve coding problems. Basic Go programming experience will be an added advantage.

Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games Key Features * Apply the power of deep learning to complex reasoning tasks by building a Game AI * Exploit the most recent developments in machine learning and AI for building smart games * Implement deep learning models and neural networks with Python Book Description The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning. What you will learn * Learn the foundations of neural networks and deep learning. * Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots. * Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems. * Working with Unity ML-Agents toolkit and how to install, setup and run the kit. * Understand core concepts of DRL and the differences between discrete and continuous action environments. * Use several advanced forms of learning in various scenarios from developing agents to testing games. Who this book is for This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.

Understand the fundamentals of C# programming and get started with coding from ground up in an engaging and practical manner Key Features * Beginner's guide to getting started with software development concepts from a macro level * Leverage the power of the latest C# in solving the complex programming problems * Learn to script and customize your 3D games and implement animation techniques to make them engaging Book Description Learning to program in today’s technical landscape can be a daunting task, especially when faced with the sheer number of languages you have to choose from. Luckily, Learning C# with Unity 2019 removes the guesswork and starts you off on the path to becoming a confident, and competent, programmer using game development with Unity. You’ll start off small by learning the building blocks of programming, from variables, methods, and conditional statements to classes and object-oriented systems. After you have the basics under your belt you’ll explore the Unity interface, creating C# scripts, and translating your newfound knowledge into simple game mechanics. Throughout this journey, you’ll get hands-on experience with programming best practices and macro-level topics such as manager classes and flexible application architecture. By the end of the book, you’ll be familiar with intermediate C# topics like generics, delegates, and events, setting you up to take on projects of your own. What you will learn * Understand programming fundamentals with practice examples in C# * Explore the interface and features of Unity 2019 * Learn C# programming syntax from scratch * Create a game design document and prototype level * Explore intermediate programming topics and best practices * Implement game mechanics, interactions, and UI elements with C# Who this book is for The book caters to developers and programmers who want to get started with C# programming in a fun and engaging manner. Anyone who wants to build games and script in C# language and Unity can take this book up. No prior programming or Unity experience is required.

Effectively implement and administer business solutions on any scale in a cost-effective way to have a competitive advantage using Gsuite Key Features * Enhance administration with Admin console and Google Apps Script * Prepare for the G suite certification using the concepts in the book * Learn how to use reports to monitor, troubleshoot and optimize G Suite Book Description Hands-On G Suite for Administrators is a comprehensive hands-on guide to G Suite Administration that will prepare you with all you need to know to become a certified G Suite Administrator, ready to handle all the business scales, from a small office to a large enterprise. You will start by learning the main features, tools, and services from G Suite for Business and then, you will explore all it has to offer and the best practices, so you can make the most out of it. We will explore G Suite tools in depth so you and your team get everything you need -combination of tools, settings and practices- to succeed in an intuitive, safe and collaborative way. While learning G Suite tools you will also learn how to use Google Sites and App Maker, to create from your corporate site to internal tools, live reports that seamlessly integrate with live documents, and advanced Google Services. Finally, you will learn how to set up, analyze and enforce Security, Privacy for your business and how to efficiently troubleshoot a wide variety of issues. What you will learn * Setting up G Suite for the business account * Work with the advanced setup of additional business domains and administrate users in multiple * Explore Guite's extensive set of features to cover your team’s creation and collaboration needs * Setup, manage and analyze your security to prevent, find or fix any security problem in G Suite * Manage Mobile devices and integrate with third-party apps * Create cloud documents, working alone or collaborating in real time Who this book is for System administrators, cloud administrators, business professionals, and aspirants of G Suite admin certificate wanting to master implementing G Suite tools for various admin tasks and effectively implement the G Suite administration for business

Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more. Key Features * Use R 3.5 to implement real-world examples in machine learning * Implement key machine learning algorithms to understand the working mechanism of smart models * Create end-to-end machine learning pipelines using modern libraries from the R ecosystem Book Description Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R. What you will learn * Introduce yourself to the basics of machine learning with R 3.5 * Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results * Learn to build predictive models with the help of various machine learning techniques * Use R to visualize data spread across multiple dimensions and extract useful features * Use interactive data analysis with R to get insights into data * Implement supervised and unsupervised learning, and NLP using R libraries Who this book is for This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.

Create end-to-end systems that can power robots with artificial vision and deep learning techniques Key Features * Study ROS, the main development framework for robotics, in detail * Learn all about convolutional neural networks, recurrent neural networks, and robotics * Create a chatbot to interact with the robot Book Description Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video. By the end of this book, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment. What you will learn * Explore the ROS and build a basic robotic system * Understand the architecture of neural networks * Identify conversation intents with NLP techniques * Learn and use the embedding with Word2Vec and GloVe * Build a basic CNN and improve it using generative models * Use deep learning to implement artificial intelligence(AI)and object recognition * Develop a simple object recognition system using CNNs * Integrate AI with ROS to enable your robot to recognize objects Who this book is for Artificial Vision and Language Processing for Robotics is for robotics engineers who want to learn how to integrate computer vision and deep learning techniques to create complete robotic systems. It will prove beneficial to you if you have working knowledge of Python and a background in deep learning. Knowledge of the ROS is a plus.

Work with the powerful subscription software, Office 365 to increase your organization's efficiency by managing file sharing, email exchange and much more. Key Features * Become well versed with Office 365 and leverage its capabilities for your business * Speed up your workflow and effectively collaborate using Office Web Apps * Learn to set audio and web conferences and seamlessly access your workspace Book Description Microsoft Office 365 combines the popular Office suite with next-generation cloud computing capabilities. With this user guide, you'll be able to implement its software features for effective business communication and collaboration. This book begins by providing you with a quick introduction to the user interface (UI) and the most commonly used features of Office 365. After covering the core aspects of this suite, you'll learn how to perform various email functions via Exchange. Next, you will learn how to communicate using Skype for Business and Microsoft Teams. To boost your productivity, this book will help you learn everything from using instant messaging to conducting audio and web conferences, and even accessing business information from any location. In the final chapters, you will learn to work in a systematic style using file management and collaboration with OneDrive for Business using SharePoint. By the end of this book, you'll be equipped with the knowledge you need to take full advantage of Office 365 and level up your organization's productivity. What you will learn * Understand the UI of Office 365 * Perform a variety of email functions through Exchange * Communicate using Skype for Business and Microsoft Teams * Explore file management using OneDrive for Business * Collaborate using SharePoint * Understand how to leverage Office 365 in your daily tasks Who this book is for If you are an IT professional who wants to upgrade your traditional Office suite, this book is for you. Users looking to learn, configure, manage, and maintain an Office 365 environment in their organization will also find this book useful. Some understanding of Microsoft Office Suite and cloud computing basics will be beneficial.