Biznes IT

Learning Bayesian Models with R. Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

Hari Manassery Koduvely

Bayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources. With the recent advances in computation and several open sources packages available in R, Bayesian modeling has become more feasible to use for practical applications today. Therefore, it would be advantageous for all data scientists and engineers to understand Bayesian methods and apply them in their projects to achieve better results.Learning Bayesian Models with R starts by giving you a comprehensive coverage of the Bayesian Machine Learning models and the R packages that implement them. It begins with an introduction to the fundamentals of probability theory and R programming for those who are new to the subject. Then the book covers some of the important machine learning methods, both supervised and unsupervised learning, implemented using Bayesian Inference and R.Every chapter begins with a theoretical description of the method explained in a very simple manner. Then, relevant R packages are discussed and some illustrations using data sets from the UCI Machine Learning repository are given. Each chapter ends with some simple exercises for you to get hands-on experience of the concepts and R packages discussed in the chapter.The last chapters are devoted to the latest development in the field, specifically Deep Learning, which uses a class of Neural Network models that are currently at the frontier of Artificial Intelligence. The book concludes with the application of Bayesian methods on Big Data using the Hadoop and Spark frameworks.

Learning Bitcoin. Embrace the new world of fiance by leveraging the power of crypto-currencies using Bitcoin and the Blockchain

Richard Caetano

The financial crisis of 2008 raised attention to the need for transparency and accountability in the financial world. As banks and governments were scrambling to stay solvent while seeking a sustainable plan, a powerfully new and resilient technology emerged.Bitcoin, built on a fundamentally new technology called “The Blockchain,” offered the promise of a new financial system where transactions are sent directly between two parties without the need for central control.Bitcoin exists as an open and transparent financial system without banks, governments, or corporate support. Simply put, Bitcoin is “programmable money” that has the potential to change the world on the same scale as the Internet itself.This book arms you with immense knowledge of Bitcoin and helps you implement the technology in your money matters, enabling secure transactions.We first walk through the fundamentals of Bitcoin, illustrate how the technology works, and exemplify how to interact with this powerful and new financial technology. You will learn how to set up your online Bitcoin wallet, indulge in buying and selling of bitcoins, and manage their storage. We then get to grips with the most powerful algorithm of all times: the Blockchain, and learn how crypto-currencies can reduce the risk of fraud for e-commerce merchants and consumers.With a solid base of Blockchain, you will write and execute your own custom transactions. Most importantly, you will be able to protect and secure your Bitcoin with the help of effective solutions provided in the book. Packed with plenty of screenshots, Learning Bitcoin is a simple and painless guide to working with Bitcoin.

Learning DevOps. The complete guide to accelerate collaboration with Jenkins, Kubernetes, Terraform and Azure DevOps

Mikael Krief

The implementation of DevOps processes requires the efficient use of various tools, and the choice of these tools is crucial for the sustainability of projects and collaboration between development (Dev) and operations (Ops). This book presents the different patterns and tools that you can use to provision and configure an infrastructure in the cloud. You'll begin by understanding DevOps culture, the application of DevOps in cloud infrastructure, provisioning with Terraform, configuration with Ansible, and image building with Packer. You'll then be taken through source code versioning with Git and the construction of a DevOps CI/CD pipeline using Jenkins, GitLab CI, and Azure Pipelines. This DevOps handbook will also guide you in containerizing and deploying your applications with Docker and Kubernetes. You'll learn how to reduce deployment downtime with blue-green deployment and the feature flags technique, and study DevOps practices for open source projects. Finally, you'll grasp some best practices for reducing the overall application lead time to ensure faster time to market. By the end of this book, you'll have built a solid foundation in DevOps, and developed the skills necessary to enhance a traditional software delivery process using modern software delivery tools and techniques

Learning Einstein Analytics. Unlock critical insights with Salesforce Einstein Analytics

Santosh Tukaram Chitalkar

Salesforce Einstein analytics aka Wave Analytics is a cloud-based platform which connects data from the multiple sources and explores it to uncover insights. It empowers sales reps, marketers, and analysts with the insights to make customer interactions smarter, without building mathematical models. You will learn to create app, lenses, dashboards and share dashboards with other users.This book starts off with explaining you fundamental concepts like lenses, step, measures and sets you up with Einstein Analytics platform. We then move on to creating an app and here you will learn to create datasets, dashboards and different ways to import data into Analytics. Moving on we look at Einstein for sales, services, and marketing individually. Here you will learn to manage your pipeline, understand important business drivers and visualize trends. You will also learn features related to data monitoring tools and embedding dashboards with lightning, visualforce page and mobile devices. Further, you will learn advanced features pertaining to recent advancements in Einstein which include machine learning constructs and getting predictions for events. By the end of this book, you will become proficient in the Einstein analytics, getting insights faster and understanding your customer in a better way.

Learning Elastic Stack 6.0. A beginner's guide to distributed search, analytics, and visualization using Elasticsearch, Logstash and Kibana

Pranav Shukla, Sharath Kumar

The Elastic Stack is a powerful combination of tools for distributed search, analytics, logging, and visualization of data from medium to massive data sets. The newly released Elastic Stack 6.0 brings new features and capabilities that empower users to find unique, actionable insights through these techniques. This book will give you a fundamental understanding of what the stack is all about, and how to use it efficiently to build powerful real-time data processing applications.After a quick overview of the newly introduced features in Elastic Stack 6.0, you’ll learn how to set up the stack by installing the tools, and see their basic configurations. Then it shows you how to use Elasticsearch for distributed searching and analytics, along with Logstash for logging, and Kibana for data visualization. It also demonstrates the creation of custom plugins using Kibana and Beats. You’ll find out about Elastic X-Pack, a useful extension for effective security and monitoring. We also provide useful tips on how to use the Elastic Cloud and deploy the Elastic Stack in production environments.On completing this book, you’ll have a solid foundational knowledge of the basic Elastic Stack functionalities. You’ll also have a good understanding of the role of each component in the stack to solve different data processing problems.

Learning Google BigQuery. A beginner's guide to mining massive datasets through interactive analysis

Thirukkumaran Haridass, Eric Brown

Google BigQuery is a popular cloud data warehouse for large-scale data analytics. This book will serve as a comprehensive guide to mastering BigQuery, and how you can utilize it to quickly and efficiently get useful insights from your Big Data.You will begin with getting a quick overview of the Google Cloud Platform and the various services it supports. Then, you will be introduced to the Google BigQuery API and how it fits within in the framework of GCP. The book covers useful techniques to migrate your existing data from your enterprise to Google BigQuery, as well as readying and optimizing it for analysis. You will perform basic as well as advanced data querying using BigQuery, and connect the results to various third party tools for reporting and visualization purposes such as R and Tableau. If you're looking to implement real-time reporting of your streaming data running in your enterprise, this book will also help you.This book also provides tips, best practices and mistakes to avoid while working with Google BigQuery and services that interact with it. By the time you're done with it, you will have set a solid foundation in working with BigQuery to solve even the trickiest of data problems.

Learning Informatica PowerCenter 10.x. Enterprise data warehousing and intelligent data centers for efficient data management solutions - Second Edition

Rahul Malewar

Informatica PowerCenter is an industry-leading ETL tool, known for its accelerated data extraction, transformation, and data management strategies. This book will be your quick guide to exploring Informatica PowerCenter’s powerful features such as working on sources, targets, transformations, performance optimization, scheduling, deploying for processing, and managing your data at speed. First, you’ll learn how to install and configure tools. You will learn to implement various data warehouse and ETL concepts, and use PowerCenter 10.x components to build mappings, tasks, workflows, and so on. You will come across features such as transformations, SCD, XML processing, partitioning, constraint-based loading, Incremental aggregation, and many more. Moreover, you’ll also learn to deliver powerful visualizations for data profiling using the advanced monitoring dashboard functionality offered by the new version. Using data transformation technique, performance tuning, and the many new advanced features, this book will help you understand and process data for training or production purposes. The step-by-step approach and adoption of real-time scenarios will guide you through effectively accessing all core functionalities offered by Informatica PowerCenter version 10.x.

Learning Microsoft Cognitive Services. Create intelligent apps with vision, speech, language, and knowledge capabilities using Microsoft Cognitive Services

Leif Larsen Henning Larsen

Take your app development to the next level with Learning Microsoft Cognitive Services. Using Leif's knowledge of each of the powerful APIs, you'll learn how to create smarter apps with more human-like capabilities. ? Discover what each API has to offer and learn how to add it to your app ? Study each AI using theory and practical examples ? Learn current API best practices