Leverage benefits of machine learning techniques using Python About This Book Improve and optimise machine learning systems using effective strategies. Develop a strategy to deal with a large amount of data. Use of Python code for implementing a range of machine learning algorithms and techniques. Who This Book Is For This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts. What You Will Learn Learn to write clean and elegant Python code that will optimize the strength of your algorithms Uncover hidden patterns and structures in data with clustering Improve accuracy and consistency of results using powerful feature engineering techniques Gain practical and theoretical understanding of cutting-edge deep learning algorithms Solve unique tasks by building models Get grips on the machine learning design process In Detail Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering. Style and approach This course includes all the resources that will help you jump into the data science field with Python. The aim is to walk through the elements of Python covering powerful machine learning libraries. This course will explain important machine learning models in a step-by-step manner. Each topic is well explained with real-world applications with detailed guidance.Through this comprehensive guide, you will be able to explore machine learning techniques.

Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. This is a Packt Instant guide, which provides concise and clear recipes to create PLC programs using RSLogix 5000.The purpose of this book is to capture the core elements of PLC programming with RSLogix 5000 so that electricians, instrumentation techs, automation professionals, and students who are familiar with basic PLC programming techniques can come up to speed with a minimal investment of time and energy.

Leverage the power of Python and its de facto GUI framework to build highly interactive interfaces About This Book ? The fundamentals of Python and GUI programming with Tkinter. ? Create multiple cross-platform projects by integrating a host of third-party libraries and tools. ? Build beautiful and highly-interactive user interfaces that target multiple devices. Who This Book Is For This book is for beginners to GUI programming who haven’t used Tkinter yet and are eager to start building great-looking and user-friendly GUIs. Prior knowledge of Python programming is expected. What You Will Learn ? Create a scrollable frame via theCanvas widget ? Use the pack geometry manager andFrame widget to control layout ? Learn to choose a data structurefor a game ? Group Tkinter widgets, such asbuttons, canvases, and labels ? Create a highly customizablePython editor ? Design and lay out a chat window In Detail Tkinter is a modular, cross-platform application development toolkit for Python. When developing GUI-rich applications, the most important choices are which programming language(s) and which GUI framework to use. Python and Tkinter prove to be a great combination. This book will get you familiar with Tkinter by having you create fun and interactive projects. These projects have varying degrees of complexity. We'll start with a simple project, where you'll learn the fundamentals of GUI programming and the basics of working with a Tkinter application. After getting the basics right, we'll move on to creating a project of slightly increased complexity, such as a highly customizable Python editor. In the next project, we'll crank up the complexity level to create an instant messaging app. Toward the end, we'll discuss various ways of packaging our applications so that they can be shared and installed on other machines without the user having to learn how to install and run Python programs. Style and approach Step by Step guide with real world examples

Discover interesting recipes to help you understand the concepts of object detection, image processing, and facial detection Key Features * Explore the latest features and APIs in OpenCV 4 and build computer vision algorithms * Develop effective, robust, and fail-safe vision for your applications * Build computer vision algorithms with machine learning capabilities Book Description OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work through recipes that implement a variety of tasks, such as facial recognition and detection. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs. This book begins by setting up OpenCV, and explains how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection. By the end of the book, you'll be able to confidently implement a range to computer vision algorithms to meet the technical requirements of your complex CV projects What you will learn * Install and create a program using the OpenCV library * Segment images into homogenous regions and extract meaningful objects * Apply image filters to enhance image content * Exploit image geometry to relay different views of a pictured scene * Calibrate the camera from different image observations * Detect people and objects in images using machine learning techniques * Reconstruct a 3D scene from images * Explore face detection using deep learning Who this book is for If you’re a CV developer or professional who already uses or would like to use OpenCV for building computer vision software, this book is for you. You’ll also find this book useful if you’re a C++ programmer looking to extend your computer vision skillset by learning OpenCV.

Teach your machine to think for itself! Key Features * Delve into supervised learning and grasp how a machine learns from data * Implement popular machine learning algorithms from scratch, developing a deep understanding along the way * Explore some of the most popular scientific and mathematical libraries in the Python language Book Description Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems. What you will learn * Crack how a machine learns a concept and generalize its understanding to new data * Uncover the fundamental differences between parametric and non-parametric models * Implement and grok several well-known supervised learning algorithms from scratch * Work with models in domains such as ecommerce and marketing * Expand your expertise and use various algorithms such as regression, decision trees, and clustering * Build your own models capable of making predictions * Delve into the most popular approaches in deep learning such as transfer learning and neural networks Who this book is for This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.

This is a book for Business Analysts (BAs) who need to develop a process model for implementation in a business process management system. Developers looking at the JBoss jBPM toolset will also find it a useful introduction to the key concepts. This book is a full toolkit for someone who wants to implement BPM in the right way. This toolkit is particularly aimed at Business Analysts, although Project Managers, IT managers, developers, and even business people can expect to find useful tools and techniques in here. We will present the project framework, analysis techniques and templates, BPM technology and example deliverables that you need to successfully bring a BPM solution into your organization.

Microsoft .NET Framework 4.5 First Look Cookbook

Exploit design, deployment, and management of large-scale containers About This Book ? Explore the latest features available in Kubernetes 1.10 ? Ensure that your clusters are always available, scalable, and up to date ? Master the skills of designing and deploying large clusters on various cloud platforms Who This Book Is For Mastering Kubernetes is for you if you are a system administrator or a developer who has an intermediate understanding of Kubernetes and wish to master its advanced features. Basic knowledge of networking would also be helpful. In all, this advanced-level book provides a smooth pathway to mastering Kubernetes. What You Will Learn ? Architect a robust Kubernetes cluster for long-time operation ? Discover the advantages of running Kubernetes on GCE, AWS, Azure, and bare metal ? Understand the identity model of Kubernetes, along with the options for cluster federation ? Monitor and troubleshoot Kubernetes clusters and run a highly available Kubernetes ? Create and configure custom Kubernetes resources and use third-party resources in your automation workflows ? Enjoy the art of running complex stateful applications in your container environment ? Deliver applications as standard packages In Detail Kubernetes is an open source system that is used to automate the deployment, scaling, and management of containerized applications. If you are running more containers or want automated management of your containers, you need Kubernetes at your disposal. To put things into perspective, Mastering Kubernetes walks you through the advanced management of Kubernetes clusters. To start with, you will learn the fundamentals of both Kubernetes architecture and Kubernetes design in detail. You will discover how to run complex stateful microservices on Kubernetes including advanced features such as horizontal pod autoscaling, rolling updates, resource quotas, and persistent storage backend. Using real-world use cases, you will explore the options for network configuration, and understand how to set up, operate, and troubleshoot various Kubernetes networking plugins. In addition to this, you will get to grips with custom resource development and utilization in automation and maintenance workflows. To scale up your knowledge of Kubernetes, you will encounter some additional concepts based on the Kubernetes 1.10 release, such as Promethus, Role-based access control, API aggregation, and more. By the end of this book, you’ll know everything you need to graduate from intermediate to advanced level of understanding Kubernetes. Style and approach Delving into the design of the Kubernetes platform, the reader will be exposed to Kubernetes advanced features and best practices. This advanced-level book will provide a pathway to mastering Kubernetes.

Java: Beginner's Guide to Programming Code with Java

Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch Key Features * Learn and implement machine learning algorithms in a variety of real-life scenarios * Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques * Find easy-to-follow code solutions for tackling common and not-so-common challenges Book Description This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples. What you will learn * Use predictive modeling and apply it to real-world problems * Explore data visualization techniques to interact with your data * Learn how to build a recommendation engine * Understand how to interact with text data and build models to analyze it * Work with speech data and recognize spoken words using Hidden Markov Models * Get well versed with reinforcement learning, automated ML, and transfer learning * Work with image data and build systems for image recognition and biometric face recognition * Use deep neural networks to build an optical character recognition system Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.

The book uses stepbystep instructions along with full code listings for each exercise. After each exercise, the author pauses to reflect, explain, and offer insights before building on the project. The author approaches the content with the belief that we are all teachers and that you are reading this book not only because you want to learn, but because you want to share your knowledge with others. Motivated students can pick up this book and teach themselves how to program because the book takes a simple, strategic, and structured approach to learning Scratch. Parents can grasp the fundamentals so that they can guide their children through introductory Scratch programming exercises. It’s perfect for homeschool families. Teachers of all disciplines from computer science to English can quickly get up to speed with Scratch and adapt the projects for use in the classroom.

Java: Tips and Tricks to Programming Code with Java

Over 79 hands-on recipes for professional embedded Linux developers to optimize and boost their Yocto Project know-how About This Book ? Optimize your Yocto setup to speed up development and debug build issues ? Use what is quickly becoming the standard embedded Linux product builder framework—the Yocto Project ? Recipe-based implementation of best practices to optimize your Linux system Who This Book Is For If you are an embedded Linux developer with the basic knowledge of Yocto Project, this book is an ideal way to broaden your knowledge with recipes for embedded development. What You Will Learn ? Optimize your Yocto Project setup to speed up development and debug build issues ? Use Docker containers to build Yocto Project-based systems ? Take advantage of the user-friendly Toaster web interface to the Yocto Project build system ? Build and debug the Linux kernel and its device trees ? Customize your root filesystem with already-supported and new Yocto packages ? Optimize your production systems by reducing the size of both the Linux kernel and root filesystems ? Explore the mechanisms to increase the root filesystem security ? Understand the open source licensing requirements and how to comply with them when cohabiting with proprietary programs ? Create recipes, and build and run applications in C, C++, Python, Node.js, and Java In Detail The Yocto Project has become the de facto distribution build framework for reliable and robust embedded systems with a reduced time to market. You'll get started by working on a build system where you set up Yocto, create a build directory, and learn how to debug it. Then, you'll explore everything about the BSP layer, from creating a custom layer to debugging device tree issues. In addition to this, you’ll learn how to add a new software layer, packages, data, *s, and configuration files to your system. You will then cover topics based on application development, such as using the Software Development Kit and how to use the Yocto project in various development environments. Toward the end, you will learn how to debug, trace, and profile a running system. This second edition has been updated to include new content based on the latest Yocto release. Style and approach This recipe-based book will guide you through all the development stages of an embedded Linux product design using the Yocto Project.

A solution-based guide to put your deep learning models into production with the power of Apache Spark Key Features * Discover practical recipes for distributed deep learning with Apache Spark * Learn to use libraries such as Keras and TensorFlow * Solve problems in order to train your deep learning models on Apache Spark Book Description With deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed. With the help of the Apache Spark Deep Learning Cookbook, you’ll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you’ll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you’ll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras. By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark. What you will learn * Set up a fully functional Spark environment * Understand practical machine learning and deep learning concepts * Apply built-in machine learning libraries within Spark * Explore libraries that are compatible with TensorFlow and Keras * Explore NLP models such as Word2vec and TF-IDF on Spark * Organize dataframes for deep learning evaluation * Apply testing and training modeling to ensure accuracy * Access readily available code that may be reusable Who this book is for If you’re looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python is a plus.

This book gets you started and gives you knowledge about AWS Fargate in order to successfully incorporate it in your ECS container application. Key Features *Gives you a quick walk-through over the Amazon Elastic Container Services (ECS) *Provides an in depth knowledge of the components that Amazon Fargate has to offer. *Learn the practical aspects of Docker application development with a managed service Book Description Amazon Fargate is new launch type for the Amazon Elastic Container Service (ECS). ECS is an AWS service for Docker container orchestration. Docker is the de facto containerization framework and has revolutionized packaging and deployment of software. The introduction of Fargate has made the ECS platform serverless. The book takes you through how Amazon Fargate runs ECS services composed of tasks and Docker containers and exposes the containers to the user. Fargate has simplified the ECS platform. We will learn how Fargate creates an Elastic Network Interface (ENI) for each task and how auto scaling can be enabled for ECS tasks. You will also learn about using an IAM policy to download Docker images and send logs to CloudWatch. Finally, by the end of this book, you will have learned about how to use ECS CLI to create an ECS cluster and deploy tasks with Docker Compose. What you will learn *Running Docker containers with a managed service *Use Amazon ECS in Fargate launch mode *Configure CloudWatch Logging with Fargate *Use an IAM Role with Fargate *Understand how ECS CLI is used with Fargate *Learn how to use an Application Load Balancer with Fargate *Learn about Auto Scaling with Fargate Who this book is for This book is for Docker users and developers who want to learn about the Fargate platform. Typical job roles for which the book is suitable are DevOps Architect, Docker Engineer, and AWS Cloud Engineer. Prior knowledge of AWS and ECS is helpful but not mandatory.

Understand the Ethereum platform to build distributed applications that are secured and decentralized using blockchain technology Key Features *Build your own decentralized applications using real-world blockchain examples *Implement Ethereum for building smart contracts and cryptocurrency applications with easy-to-follow projects *Enhance your application security with blockchain Book Description Ethereum enables the development of efficient, smart contracts that contain code. These smart contracts can interact with other smart contracts to make decisions, store data, and send Ether to others.Ethereum Projects for Beginners provides you with a clear introduction to creating cryptocurrencies, smart contracts, and decentralized applications. As you make your way through the book, you’ll get to grips with detailed step-by-step processes to build advanced Ethereum projects. Each project will teach you enough about Ethereum to be productive right away. You will learn how tokenization works, think in a decentralized way, and build blockchain-based distributed computing systems. Towards the end of the book, you will develop interesting Ethereum projects such as creating wallets and secure data sharing.By the end of this book, you will be able to tackle blockchain challenges by implementing end-to-end projects using the full power of the Ethereum blockchain. What you will learn *Develop your ideas fast and efficiently using the Ethereum blockchain *Make writing and deploying smart contracts easy and manageable *Work with private data in blockchain applications *Handle large files in blockchain applications *Ensure your decentralized applications are safe *Explore how Ethereum development frameworks work *Create your own cryptocurrency or token on the Ethereum blockchain *Make sure your cryptocurrency is ERC20-compliant to launch an ICO Who this book is for This book is for individuals who want to build decentralized applications using blockchain technology and the power of Ethereum from scratch. Some prior knowledge of JavaScript is required, since most examples use a web frontend.

Enhance your skills in expert module development, deployment, security, DevOps, and cloud Key Features *A step-by-step guide to get you started with PowerShell Core 6.0 *Harness the capabilities of PowerShell Core 6.0 to perform simple to complex administration tasks *Learn core administrative concepts such as scripting, pipelines, and DSC Book Description Beginning with an overview of the different versions of PowerShell, Learn PowerShell Core 6.0 introduces you to VSCode and then dives into helping you understand the basic techniques in PowerShell scripting. You will cover advanced coding techniques, learn how to write reusable code as well as store and load data with PowerShell. This book will help you understand PowerShell security and Just Enough Administration, enabling you to create your own PowerShell repository. The last set of chapters will guide you in setting up, configuring, and working with Release Pipelines in VSCode and VSTS, and help you understand PowerShell DSC. In addition to this, you will learn how to use PowerShell with Windows, Azure, Microsoft Online Services, SCCM, and SQL Server. The final chapter will provide you with some use cases and pro tips. By the end of this book, you will be able to create professional reusable code using security insight and knowledge of working with PowerShell Core 6.0 and its most important capabilities. What you will learn *Get to grips with Powershell Core 6.0 *Explore basic and advanced PowerShell scripting techniques *Get to grips with Windows PowerShell Security *Work with centralization and DevOps with PowerShell *Implement PowerShell in your organization through real-life examples *Learn to create GUIs and use DSC in production Who this book is for If you are a Windows administrator or a DevOps user who wants to leverage PowerShell to automate simple to complex tasks, then this book is for you. Whether you know nothing about PowerShell or just enough to get by, this guide will give you what you need to go to take your scripting to the next level. You’ll also find this book useful if you’re a PowerShell expert looking to expand your knowledge in areas such as PowerShell Security and DevOps.

Explore powerful R packages to create predictive models using ensemble methods Key Features *Implement machine learning algorithms to build ensemble-efficient models *Explore powerful R packages to create predictive models using ensemble methods *Learn to build ensemble models on large datasets using a practical approach Book Description Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples. What you will learn *Carry out an essential review of re-sampling methods, bootstrap, and jackknife *Explore the key ensemble methods: bagging, random forests, and boosting *Use multiple algorithms to make strong predictive models *Enjoy a comprehensive treatment of boosting methods *Supplement methods with statistical tests, such as ROC *Walk through data structures in classification, regression, survival, and time series data *Use the supplied R code to implement ensemble methods *Learn stacking method to combine heterogeneous machine learning models Who this book is for This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.

Make the most of Angular by leveraging design patterns and best practices to build stable and high performing apps Key Features *Get to grips with the benefits and applicability of using different design patterns in Angular with the help of real-world examples *Identify and prevent common problems, programming errors, and anti-patterns *Packed with easy-to-follow examples that can be used to create reusable code and extensible designs Book Description This book is an insightful journey through the most valuable design patterns, and it will provide clear guidance on how to use them effectively in Angular. You will explore some of the best ways to work with Angular and how to use it to meet the stability and performance required in today's web development world. You’ll get to know some Angular best practices to improve your productivity and the code base of your application. We will take you on a journey through Angular designs for the real world, using a combination of case studies, design patterns to follow, and anti-patterns to avoid. By the end of the book, you will understand the various features of Angular, and will be able to apply well-known, industry-proven design patterns in your work. What you will learn *Understand Angular design patterns and anti-patterns *Implement the most useful GoF patterns for Angular *Explore some of the most famous navigational patterns for Angular *Get to know and implement stability patterns *Explore and implement operations patterns *Explore the official best practices for Angular *Monitor and improve the performance of Angular applications Who this book is for If you want to increase your understanding of Angular and apply it to real-life application development, then this book is for you.

Master serverless architectures in Python and their implementation, with Zappa on three different frameworks. Key Features * Scalable serverless Python web services using Django, Flask, and Pyramid. * Learn Asynchronous task execution on AWS Lambda and scheduling using Zappa. * Implementing Zappa in a Docker container. Book Description Serverless applications are becoming very popular these days, not just because they save developers the trouble of managing the servers, but also because they provide several other benefits such as cutting heavy costs and improving the overall performance of the application. This book will help you build serverless applications in a quick and efficient way. We begin with an introduction to AWS and the API gateway, the environment for serverless development, and Zappa. We then look at building, testing, and deploying apps in AWS with three different frameworks--Flask, Django, and Pyramid. Setting up a custom domain along with SSL certificates and configuring them with Zappa is also covered. A few advanced Zappa settings are also covered along with securing Zappa with AWS VPC. By the end of the book you will have mastered using three frameworks to build robust and cost-efficient serverless apps in Python. What you will learn *Build, test, and deploy a simple web service using AWS CLI *Integrate Flask-based Python applications, via AWS CLI configuration *Design Rest APIs integrated with Zappa for Flask and Django *Create a project in the Pyramid framework and configure it with Zappa *Generate SSL Certificates using Amazon Certificate Manager *Configure custom domains with AWS Route 53 *Create a Docker container similar to AWS Lambda Who this book is for Python Developers who are interested in learning how to develop fast and highly scalable serverless applications in Python, will find this book useful

Plan, track, and release great software Key Features * Learn to create reports and dashboard for effective project management * Implement your development strategy in JIRA. * Practices to help you manage the issues in the development team Book Description As teams scale in size, project management can get very complicated. One of the best tools to deal with this kind of problem is JIRA. This book will start by organizing your project requirements and the principles of Agile development to get you started. You will then be introduced to set up a JIRA account and the JIRA ecosystem to help you implement a dashboard for your team's work and issues. You will learn how to manage any issues and bugs that might emerge in the development stage. Going ahead, the book will help you build reports and use them to plan the releases based on the study of the reports. Towards the end, you will come across working with the gathered data and create a dashboard that helps you track the project's development. What you will learn * Create your first project (and manage existing projects) in JIRA * Manage your board view and backlogs in JIRA * Run a Scrum Sprint project in JIRA * Create reports (including topic-based reports) * Forecast using versions * Search for issues with JIRA Query Language (JQL) * Execute bulk changes to issues * Create custom filters, dashboards, and widgets * Create epics, stories, bugs, and tasks Who this book is for This book is for administrators who wants to apply the Agile approach to managing the issues, bugs, and releases in their software development projects using JIRA.