Design, develop, and deploy innovative forensic solutions using Python Key Features * Discover how to develop Python scripts for effective digital forensic analysis * Master the skills of parsing complex data structures with Python libraries * Solve forensic challenges through the development of practical Python scripts Book Description Digital forensics plays an integral role in solving complex cybercrimes and helping organizations make sense of cybersecurity incidents. This second edition of Learning Python for Forensics illustrates how Python can be used to support these digital investigations and permits the examiner to automate the parsing of forensic artifacts to spend more time examining actionable data. The second edition of Learning Python for Forensics will illustrate how to develop Python scripts using an iterative design. Further, it demonstrates how to leverage the various built-in and community-sourced forensics scripts and libraries available for Python today. This book will help strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials. By the end of this book, you will build a collection of Python scripts capable of investigating an array of forensic artifacts and master the skills of extracting metadata and parsing complex data structures into actionable reports. Most importantly, you will have developed a foundation upon which to build as you continue to learn Python and enhance your efficacy as an investigator. What you will learn * Learn how to develop Python scripts to solve complex forensic problems * Build scripts using an iterative design * Design code to accommodate present and future hurdles * Leverage built-in and community-sourced libraries * Understand the best practices in forensic programming * Learn how to transform raw data into customized reports and visualizations * Create forensic frameworks to automate analysis of multiple forensic artifacts * Conduct effective and efficient investigations through programmatic processing Who this book is for If you are a forensics student, hobbyist, or professional seeking to increase your understanding in forensics through the use of a programming language, then Learning Python for Forensics is for you. You are not required to have previous experience in programming to learn and master the content within this book. This material, created by forensic professionals, was written with a unique perspective and understanding for examiners who wish to learn programming.

Learn best practices for testing with Jira and model industry workflows that can be used during the software development lifecycle Key Features * Integrate Jira with test management tools such as Zephyr, Test Management, and SynapseRT * Understand test case management, traceability, and test execution with reports * Implement continuous integration using Jira, Jenkins, and automated testing tools Book Description Hands-On Test Management with Jira begins by introducing you to the basic concepts of Jira and takes you through real-world software testing processes followed by various organizations. As you progress through the chapters, the book explores and compares the three most popular Jira plugins—Zephyr, Test Management, and synapseRT. With this book, you’ll gain a practical understanding of test management processes using Jira. You’ll learn how to create and manage projects, create Jira tickets to manage customer requirements, and track Jira tickets. You’ll also understand how to develop test plans, test cases, and test suites, and create defects and requirement traceability matrices, as well as generating reports in Jira. Toward the end, you’ll understand how Jira can help the SQA teams to use the DevOps pipeline for automating execution and managing test cases. You’ll get to grips with configuring Jira with Jenkins to execute automated test cases in Selenium. By the end of this book, you’ll have gained a clear understanding of how to model and implement test management processes using Jira. What you will learn * Understand QMS to effectively implement quality systems in your organization * Explore a business-driven structured approach to Test Management using TMap NEXT * Implement different aspects of test planning, test strategy, and test execution * Organize and manage Agile projects in Scrum and Kanban * Uncover Jira plugins available in the Atlassian Marketplace for testing and project management * Configure a DevOps pipeline for continuous integration using Jira with Jenkins Who this book is for If you’re a quality assurance professional, software project manager, or test manager interested in learning test management best practices in your team or organization, this book is for you. Prior knowledge of test management and Jenkins will be beneficial in understanding the concepts covered in this book.

Build state-of-the-art web applications quickly and efficiently using Flask and related technologies with Python 3 Key Features * Updated to Flask 1.0.3 and Python 3.7 with coverage of Microservices * Get the most out of the powerful Flask framework and maintain the flexibility of your design choices * Write cleaner and maintainable code with the help of sample apps Book Description Flask, the lightweight Python web framework, is popular due to its powerful modular design that lets you build scalable web apps. With this recipe-based guide, you’ll explore modern solutions and best practices for Flask web development. Updated to the latest version of Flask and Python 3, this second edition of Flask Framework Cookbook moves away from some of the old and obsolete libraries and introduces recipes on bleeding edge technologies. You’ll discover different ways of using Flask to create, deploy, and manage microservices. This Flask Python book starts by covering the different configurations that a Flask application can make use of, and then helps you work with templates and learn about the ORM and view layers. You’ll also be able to write an admin interface and get to grips with debugging and logging errors. Finally, you’ll grasp a variety of deployment and post-deployment techniques for platforms such as Apache, Tornado, and Heroku. By the end of this book, you’ll have gained all the knowledge you need to write Flask applications in the best possible way and scale them using standard industry practices. What you will learn * Explore web application development in Flask, right from installation to post-deployment stages * Make use of advanced templating and data modeling techniques * Discover effective debugging, logging, and error handling techniques in Flask * Integrate Flask with different technologies such as Redis, Sentry, and MongoDB * Deploy and package Flask applications with Docker and Kubernetes * Design scalable microservice architecture using AWS LambdaContinuous integration and Continuous deployment Who this book is for If you are a web developer who wants to learn more about developing scalable and production-ready applications in Flask, this is the book for you. You’ll also find this book useful if you are already aware of Flask's major extensions and want to use them for better application development. Basic Python programming experience along with basic understanding of Flask is assumed.

Enhance your development skills with Java’s state-of-the-art features and projects to make your applications leaner and faster Key Features * Overcome the challenges involved in migrating to new versions of Java * Discover how Oracle has bridged the gap between Java and native code * Make the best use of new Java features and libraries in your applications Book Description With its new six-monthly release cadence, Java is moving forward faster. In addition to planned version releases, a lot of work is currently being undertaken on various Java projects at Oracle. In order to make best use of the new features in their applications and libraries, you must be well-versed with the most recent advancements. Java 11 and 12 – New Features will take you through the latest developments in Java, right from variable type inference and simplified multithreading through to performance improvements, which are covered in depth to help you make your applications more efficient. This book explains the relevance and applicability of Java's new features, and answers your questions on whether to invest in migrating to new Java versions and when to migrate. You'll also get to grips with platform features, such as AppCDS and new garbage collectors, to tune and optimize your application—from reduced launch time and latency to improved performance and throughput. By the end of this book, you will be equipped with a thorough understanding of the new features of Java 11, 12, and Project Amber, and possess the skills to apply them with a view to improving your application's performance. What you will learn * Study type interference and how to work with the var type * Understand Class-Data Sharing, its benefits, and limitations * Discover platform options to reduce your application’s launch time * Improve application performance by switching garbage collectors * Get up to date with the new Java release cadence * Define and assess decision criteria for migrating to a new version of Java Who this book is for If you’re an executive or solutions architect responsible for technology selection or Java migration decisions, this Java book is for you. You’ll also benefit from this book if you’re a computer science enthusiast curious to learn about the latest and upcoming Java features. This book will help you migrate your solutions from Java 8 or older to the latest Java release.

Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications Key Features * Build independent machine learning (ML) systems leveraging the best features of R 3.5 * Understand and apply different machine learning techniques using real-world examples * Use methods such as multi-class classification, regression, and clustering Book Description Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work. What you will learn * Prepare data for machine learning methods with ease * Understand how to write production-ready code and package it for use * Produce simple and effective data visualizations for improved insights * Master advanced methods, such as Boosted Trees and deep neural networks * Use natural language processing to extract insights in relation to text * Implement tree-based classifiers, including Random Forest and Boosted Tree Who this book is for This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine learning with R is mandatory.

Learn to create and customize impressive Drupal 8 modules to extend your website's functionalities Key Features * Explore a plethora of Drupal 8 APIs and get the best out of them using the power of PHP coding * Learn to implement efficient data management and data security by creating dedicated modules for it. * Stay up to date with the changes introduced in the new Drupal 8 releases Book Description Drupal 8 comes with a release cycle that allows for new functionality to be added at a much faster pace. However, this also means code deprecations and changing architecture that you need to stay on top of. This book updates the first edition and includes the new functionality introduced in versions up to, and including 8.7. The book will first introduce you to the Drupal 8 architecture and its subsystems before diving into creating your first module with basic functionality. You will work with the Drupal logging and mailing systems, learn how to output data using the theme layer and work with menus and links programmatically. Then, you will learn how to work with different kinds of data storages, create custom entities, field types and leverage the Database API for lower level database queries. You will further see how to introduce JavaScript into your module, work with the various file systems and ensure the code you write works on multilingual sites. Finally, you will learn how to programmatically work with Views, write automated tests for your functionality and also write secure code in general. By the end, you will have learned how to develop your own custom module that can provide complex business solutions. And who knows, maybe you’ll even contribute it back to the Drupal community. What you will learn * Develop Drupal 8 modules that do all the things you want * Master numerous Drupal 8 sub-systems and APIs in the process * Model, store, manipulate and process data to serve your purposes * Display data and content in a clean and secure way using the Drupal 8 theme system * Test your business logic to prevent regressions * Stay ahead of the curve and write code following the current best practices Who this book is for The primary target of this book is Drupal developers who want to learn how to write modules and develop in Drupal 8. It is also intended for Drupal site builders and PHP developers who have basic Object Oriented Programming skills. A little bit of Symfony experience is helpful but not mandatory.

Understand the nitty-gritty of Cybersecurity with ease Key Features * Align your security knowledge with industry leading concepts and tools * Acquire required skills and certifications to survive the ever changing market needs * Learn from industry experts to analyse, implement, and maintain a robust environment Book Description It's not a secret that there is a huge talent gap in the cybersecurity industry. Everyone is talking about it including the prestigious Forbes Magazine, Tech Republic, CSO Online, DarkReading, and SC Magazine, among many others. Additionally, Fortune CEO's like Satya Nadella, McAfee's CEO Chris Young, Cisco's CIO Colin Seward along with organizations like ISSA, research firms like Gartner too shine light on it from time to time. This book put together all the possible information with regards to cybersecurity, why you should choose it, the need for cyber security and how can you be part of it and fill the cybersecurity talent gap bit by bit. Starting with the essential understanding of security and its needs, we will move to security domain changes and how artificial intelligence and machine learning are helping to secure systems. Later, this book will walk you through all the skills and tools that everyone who wants to work as security personal need to be aware of. Then, this book will teach readers how to think like an attacker and explore some advanced security methodologies. Lastly, this book will deep dive into how to build practice labs, explore real-world use cases and get acquainted with various cybersecurity certifications. By the end of this book, readers will be well-versed with the security domain and will be capable of making the right choices in the cybersecurity field. What you will learn * Get an overview of what cybersecurity is and learn about the various faces of cybersecurity as well as identify domain that suits you best * Plan your transition into cybersecurity in an efficient and effective way * Learn how to build upon your existing skills and experience in order to prepare for your career in cybersecurity Who this book is for This book is targeted to any IT professional who is looking to venture in to the world cyber attacks and threats. Anyone with some understanding or IT infrastructure workflow will benefit from this book. Cybersecurity experts interested in enhancing their skill set will also find this book useful.

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.

From learning about the most sought-after design patterns to a comprehensive coverage of architectural patterns and code testing, this book is all you need to write clean, reusable code Key Features *Write clean, reusable and maintainable code, and make the most of the latest Swift version. *Analyze case studies of some of the popular open source projects and give your workflow a huge boost *Choose patterns such as MVP, MVC, and MVVM depending on the application being built Book Description Swift keeps gaining traction not only amongst Apple developers but also as a server-side language. This book demonstrates how to apply design patterns and best practices in real-life situations, whether that's for new or already existing projects. You’ll begin with a quick refresher on Swift, the compiler, the standard library, and the foundation, followed by the Cocoa design patterns – the ones at the core of many cocoa libraries – to follow up with the creational, structural, and behavioral patterns as defined by the GoF. You'll get acquainted with application architecture, as well as the most popular architectural design patterns, such as MVC and MVVM, and learn to use them in the context of Swift. In addition, you’ll walk through dependency injection and functional reactive programming. Special emphasis will be given to techniques to handle concurrency, including callbacks, futures and promises, and reactive programming. These techniques will help you adopt a test-driven approach to your workflow in order to use Swift Package Manager and integrate the framework into the original code base, along with Unit and UI testing. By the end of the book, you'll be able to build applications that are scalable, faster, and easier to maintain. What you will learn *Work efficiently with Foundation and Swift Standard library *Understand the most critical GoF patterns and use them efficiently *Use Swift 4.2 and its unique capabilities (and limitations) to implement and improve GoF patterns *Improve your application architecture and optimize for maintainability and performance *Write efficient and clean concurrent programs using futures and promises, or reactive programming techniques *Use Swift Package Manager to refactor your program into reusable components *Leverage testing and other techniques for writing robust code Who this book is for This book is for intermediate developers who want to apply design patterns with Swift to structure and scale their applications. You are expected to have basic knowledge of iOS and Swift.

Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing. Key Features *Clear and concise explanations *Gives important insights into deep learning models *Practical demonstration of key concepts Book Description PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text. By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease. What you will learn *Set up the deep learning environment using the PyTorch library *Learn to build a deep learning model for image classification *Use a convolutional neural network for transfer learning *Understand to use PyTorch for natural language processing *Use a recurrent neural network to classify text *Understand how to optimize PyTorch in multiprocessor and distributed environments *Train, optimize, and deploy your neural networks for maximum accuracy and performance *Learn to deploy production-ready models Who this book is for Developers and Data Scientist familiar with Machine Learning but new to deep learning, or existing practitioners of deep learning who would like to use PyTorch to train their deep learning models will find this book to be useful. Having knowledge of Python programming will be an added advantage, while previous exposure to PyTorch is not needed.

A comprehensive guide to building full stack applications covering frontend and server-side programming, data management, and web security Key Features * Unleash the power of React Hooks to build interactive and complex user interfaces * Build scalable full stack applications designed to meet demands of modern users * Understand how the Axios library simplifies CRUD operations Book Description React Hooks have changed the way React components are coded. They enable you to write components in a more intuitive way without using classes, which makes your code easier to read and maintain. Building on from the previous edition, this book is updated with React Hooks and the latest changes introduced in create-react-app and Spring Boot 2.1. This book starts with a brief introduction to Spring Boot. You’ll understand how to use dependency injection and work with the data access layer of Spring using Hibernate as the ORM tool. You’ll then learn how to build your own RESTful API endpoints for web applications. As you advance, the book introduces you to other Spring components, such as Spring Security to help you secure the backend. Moving on, you’ll explore React and its app development environment and components for building your frontend. Finally, you’ll create a Docker container for your application by implementing the best practices that underpin professional full stack web development. By the end of this book, you’ll be equipped with all the knowledge you need to build modern full stack applications with Spring Boot for the backend and React for the frontend. What you will learn * Create a RESTful web service with Spring Boot * Grasp the fundamentals of dependency injection and how to use it for backend development * Discover techniques for securing the backend using Spring Security * Understand how to use React for frontend programming * Benefit from the Heroku cloud server by deploying your application to it * Delve into the techniques for creating unit tests using JUnit * Explore the Material UI component library to make more user-friendly user interfaces Who this book is for If you are a Java developer familiar with Spring, but are new to building full stack applications, this is the book for you.

Understand, explore, and effectively present data using the powerful data visualization techniques of Python Key Features *Use the power of Pandas and Matplotlib to easily solve data mining issues *Understand the basics of statistics to build powerful predictive data models *Grasp data mining concepts with helpful use-cases and examples Book Description Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining. You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models. By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following Packt products: *Statistics for Machine Learning by Pratap Dangeti *Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim *Pandas Cookbook by Theodore Petrou What you will learn *Understand the statistical fundamentals to build data models *Split data into independent groups *Apply aggregations and transformations to each group *Create impressive data visualizations *Prepare your data and design models *Clean up data to ease data analysis and visualization *Create insightful visualizations with Matplotlib and Seaborn *Customize the model to suit your own predictive goals Who this book is for If you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.

A comprehensive guide to Android forensics, from setting up the workstation to analyzing key artifacts Key Features *Get up and running with modern mobile forensic strategies and techniques *Analyze the most popular Android applications using free and open source forensic tools *Learn malware detection and analysis techniques to investigate mobile cybersecurity incidents Book Description Many forensic examiners rely on commercial, push-button tools to retrieve and analyze data, even though there is no tool that does either of these jobs perfectly. Learning Android Forensics will introduce you to the most up-to-date Android platform and its architecture, and provide a high-level overview of what Android forensics entails. You will understand how data is stored on Android devices and how to set up a digital forensic examination environment. As you make your way through the chapters, you will work through various physical and logical techniques to extract data from devices in order to obtain forensic evidence. You will also learn how to recover deleted data and forensically analyze application data with the help of various open source and commercial tools. In the concluding chapters, you will explore malware analysis so that you’ll be able to investigate cybersecurity incidents involving Android malware. By the end of this book, you will have a complete understanding of the Android forensic process, you will have explored open source and commercial forensic tools, and will have basic skills of Android malware identification and analysis. What you will learn *Understand Android OS and architecture *Set up a forensics environment for Android analysis *Perform logical and physical data extractions *Learn to recover deleted data *Explore how to analyze application data *Identify malware on Android devices *Analyze Android malware Who this book is for If you are a forensic analyst or an information security professional wanting to develop your knowledge of Android forensics, then this is the book for you. Some basic knowledge of the Android mobile platform is expected.

Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x Key Features *Experimental projects showcasing the implementation of high-performance deep learning models with Keras. * *Use-cases across reinforcement learning, natural language processing, GANs and computer vision. * *Build strong fundamentals of Keras in the area of deep learning and artificial intelligence. Book Description Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems. What you will learn *Apply regression methods to your data and understand how the regression algorithm works *Understand the basic concepts of classification methods and how to implement them in the Keras environment *Import and organize data for neural network classification analysis *Learn about the role of rectified linear units in the Keras network architecture *Implement a recurrent neural network to classify the sentiment of sentences from movie reviews *Set the embedding layer and the tensor sizes of a network Who this book is for If you are a data scientist, machine learning engineer, deep learning practitioner or an AI engineer who wants to build speedy intelligent applications with minimal lines of codes, then this book is the best fit for you. Sound knowledge of machine learning and basic familiarity with Keras library would be useful.

Create distributed applications with clever design patterns to solve complex problems Key Features * Set up and run distributed algorithms on a cluster using Dask and PySpark * Master skills to accurately implement concurrency in your code * Gain practical experience of Python design patterns with real-world examples Book Description This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: * Python High Performance - Second Edition by Gabriele Lanaro * Mastering Concurrency in Python by Quan Nguyen * Mastering Python Design Patterns by Sakis Kasampalis What you will learn * Use NumPy and pandas to import and manipulate datasets * Achieve native performance with Cython and Numba * Write asynchronous code using asyncio and RxPy * Design highly scalable programs with application scaffolding * Explore abstract methods to maintain data consistency * Clone objects using the prototype pattern * Use the adapter pattern to make incompatible interfaces compatible * Employ the strategy pattern to dynamically choose an algorithm Who this book is for This Learning Path is specially designed for Python developers who want to build high-performance applications and learn about single core and multi-core programming, distributed concurrency, and Python design patterns. Some experience with Python programming language will help you get the most out of this Learning Path.

Integrate Redux with React and other front-end JavaScript frameworks efficiently and manage application states effectively Key Features * Get better at building web applications with state management using Redux * Learn the fundamentals of Redux to structure your app more efficiently * This guide will teach you develop complex apps that would be easier to maintain Book Description Starting with a detailed overview of Redux, we will follow the test-driven development (TDD) approach to develop single-page applications. We will set up JEST for testing and use JEST to test React, Redux, Redux-Sage, Reducers, and other components. We will then add important middleware and set up immutableJS in our application. We will use common data structures such as Map, List, Set, and OrderedList from the immutableJS framework. We will then add user interfaces using ReactJS, Redux-Form, and Ant Design. We will explore the use of react-router-dom and its functions. We will create a list of routes that we will need in order to create our application, and explore routing on the server site and create the required routes for our application. We will then debug our application and integrate Redux Dev tools. We will then set up our API server and create the API required for our application. We will dive into a modern approach to structuring our server site components in terms of Model, Controller, Helper functions, and utilities functions. We will explore the use of NodeJS with Express to build the REST API components. Finally, we will venture into the possibilities of extending the application for further research, including deployment and optimization. What you will learn * Follow the test-driven development (TDD) approach to develop a single-page application * Add important middleware, such as Redux store middleware, redux-saga middleware, and language middleware, to your application * Understand how to use immutableJS in your application * Build interactive components using ReactJS * Configure react-router-redux and explore the differences between react-router-dom and react-router-redux * Use Redux Dev tools to debug your application * Set up our API server and create the API required for our application Who this book is for This book is meant for JavaScript developers interesting in learning state management and building easy to maintain web applications.

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.

Leverage the features of Azure SQL database and become an expert in data management Key Features * Explore ways to create shards and elastic pools to scale Azure SQL databases * Automate common management tasks with PowerShell * Implement over 40 practical activities and exercises to reinforce your learning Book Description Despite being the cloud version of SQL Server, Azure SQL Database differs in key ways when it comes to management, maintenance, and administration. This book shows you how to administer Azure SQL database to fully benefit from its wide range of features and functionality. Professional Azure SQL Database Administration begins by covering the architecture and explaining the difference between Azure SQL Database and the on-premise SQL Server to help you get comfortable with Azure SQL database. You’ll perform common tasks such as migrating, backing up, and restoring a SQL Server database to an Azure database. As you progress, you’ll study how you can save costs and manage and scale multiple SQL Databases using elastic pools. You’ll also implement a disaster recovery solution using standard and active geo-replication. Whether it is learning different techniques to monitor and tune an Azure SQL database or improving performance using in-memory technology, this book will enable you to make the most out of Azure SQL database features and functionality for data management solutions. By the end of this book, you’ll be well versed with key aspects of an Azure SQL database instance, such as migration, backup restorations, performance optimization, high availability, and disaster recovery. What you will learn * Understand Azure SQL Database configuration and pricing options * Provision a new SQL database or migrate an existing on-premise SQL Server database to Azure SQL Database * Back up and restore Azure SQL Database * Secure an Azure SQL database * Scale an Azure SQL database * Monitor and tune an Azure SQL database * Implement high availability and disaster recovery with Azure SQL Database * Automate common management tasks with PowerShell * Develop a scalable cloud solution with Azure SQL Database * Manage, maintain, and secure managed instances Who this book is for If you’re a database administrator, database developer, or an application developer interested in developing new applications or migrating existing ones with Azure SQL database, this book is for you. Prior experience of working with an on-premise SQL Server or Azure SQL database along with a basic understanding of PowerShell scripts and C# code is necessary to grasp the concepts covered in this book.

Explore and understand data with the powerful data visualization techniques of Tableau, and then communicate insights in powerful ways Key Features *Apply best practices in data visualization and chart types exploration *Explore the latest version of Tableau Desktop with hands-on examples *Understand the fundamentals of Tableau storytelling Book Description Graphical presentation of data enables us to easily understand complex data sets. Tableau 10 Complete Reference provides easy-to-follow recipes with several use cases and real-world business scenarios to get you up and running with Tableau 10. This Learning Path begins with the history of data visualization and its importance in today's businesses. You'll also be introduced to Tableau - how to connect, clean, and analyze data in this visual analytics software. Then, you'll learn how to apply what you've learned by creating some simple calculations in Tableau and using Table Calculations to help drive greater analysis from your data. Next, you'll explore different advanced chart types in Tableau. These chart types require you to have some understanding of the Tableau interface and understand basic calculations. You’ll study in detail all dashboard techniques and best practices. A number of recipes specifically for geospatial visualization, analytics, and data preparation are also covered. Last but not least, you'll learn about the power of storytelling through the creation of interactive dashboards in Tableau. Through this Learning Path, you will gain confidence and competence to analyze and communicate data and insights more efficiently and effectively by creating compelling interactive charts, dashboards, and stories in Tableau. This Learning Path includes content from the following Packt products: *Learning Tableau 10 - Second Edition by N. Milligan *Getting Started with Tableau 2018.x by Tristan Guillevin What you will learn *Build effective visualizations, dashboards, and story points *Build basic to more advanced charts with step-by-step recipes *Become familiar row-level, aggregate, and table calculations *Dig deep into data with clustering and distribution models *Prepare and transform data for analysis *Leverage Tableau’s mapping capabilities to visualize data *Use data storytelling techniques to aid decision making strategy Who this book is for Tableau 10 Complete Reference is designed for anyone who wants to understand their data better and represent it in an effective manner. It is also used for BI professionals and data analysts who want to do better at their jobs.

Build powerful data analytics applications with this business intelligence tool and overcome all your business challenges Key Features *Master time-saving techniques and make your QlikView development more efficient *Perform geographical analysis and sentiment analysis in your QlikView applications *Explore advanced QlikView techniques, tips, and tricks to deliver complex business requirements Book Description QlikView is one of the most flexible and powerful business intelligence platforms around, and if you want to transform data into insights, it is one of the best options you have at hand. Use this Learning Path, to explore the many features of QlikView to realize the potential of your data and present it as impactful and engaging visualizations. Each chapter in this Learning Path starts with an understanding of a business requirement and its associated data model and then helps you create insightful analysis and data visualizations around it. You will look at problems that you might encounter while visualizing complex data insights using QlikView, and learn how to troubleshoot these and other not-so-common errors. This Learning Path contains real-world examples from a variety of business domains, such as sales, finance, marketing, and human resources. With all the knowledge that you gain from this Learning Path, you will have all the experience you need to implement your next QlikView project like a pro. This Learning Path includes content from the following Packt products: *QlikView for Developers by Miguel ?ngel García, Barry Harmsen *Mastering QlikView by Stephen Redmond *Mastering QlikView Data Visualization by Karl Pover What you will learn *Deliver common business requirements using advanced techniques *Load data from disparate sources to build associative data models *Understand when to apply more advanced data visualization *Utilize the built-in aggregation functions for complex calculations *Build a data architecture that supports scalable QlikView deployments *Troubleshoot common data visualization errors in QlikView *Protect your QlikView applications and data Who this book is for This Learning Path is designed for developers who want to go beyond their technical knowledge of QlikView and understand how to create analysis and data visualizations that solve real business needs. To grasp the concepts explained in this Learning Path, you should have a basic understanding of the common QlikView functions and some hands-on experience with the tool.

Step-by-step guide to build high performing predictive applications Key Features *Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects *Explore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanations *Learn to deploy a predictive model's results as an interactive application Book Description Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming. What you will learn *Get to grips with the main concepts and principles of predictive analytics *Learn about the stages involved in producing complete predictive analytics solutions *Understand how to define a problem, propose a solution, and prepare a dataset *Use visualizations to explore relationships and gain insights into the dataset *Learn to build regression and classification models using scikit-learn *Use Keras to build powerful neural network models that produce accurate predictions *Learn to serve a model's predictions as a web application Who this book is for This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.