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Automated Machine Learning with Microsoft Azure
Automated Machine Learning with Microsoft Azure

Automated Machine Learning with Microsoft Azure helps you to build high-performing, accurate machine learning models in record time. It allows anyone to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. With a series of clicks on a guided user interface (GUI), novices and seasoned data scientists alike can train and deploy machine learning solutions to production with ease. This book will teach you how to use Azure AutoML with both the GUI as well as the AzureML Python software development kit (SDK) in a careful, step-by-step way. First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train ...
Data Science Revealed
Data Science Revealed

Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification pro ...
Beginning Power Apps
Beginning Power Apps

Transform the way your business works with easy-to-build apps. With this updated and expanded second edition, you can build business apps that work with your company's systems and databases, without having to enlist the expertise of costly, professionally trained software developers. In this new edition, business applications expert Tim Leung offers step-by-step guidance on how you can improve all areas of your business. He shows how you can replace manual or paper processes with modern apps that run on phone or tablet devices. For administrative and back-office operations, he covers how to build apps with workflow and dashboard capabilities. To facilitate collaboration with customers and clients, you'll learn how to build secure web portals with data entry capabilities, including how to customize those portals with code. This hands-on new edition has 10 new chapters - including coverage on model-driven and portal apps, artificial intelligence, building components using the Power ...
Machine Learning for Financial Risk Management with Python
Machine Learning for Financial Risk Management with Python

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models; Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning; Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension; Develop a credit risk anal ...
Cloud Native Architecture and Design
Cloud Native Architecture and Design

Build enterprise-grade cloud-native systems and learn all about cloud-native architecture and design. This book provides extensive in-depth details of patterns, tools, techniques, and processes with plenty of examples. Cloud Native Architecture and Design begins by explaining the fundamentals of cloud-native architecture and services, what cloud principles and patterns to use, and details of designing a cloud-native element. The book progresses to cover the details of how IT systems can modernize to embrace cloud-native architecture, and also provides details of various enterprise assessment techniques to decide what systems can move and cannot move into the cloud. Architecting and designing a cloud-native system isn't possible without modernized software engineering principles, the culture of automation, and the culture of innovation. As such, this book covers the details of cloud-native software engineering methodologies, and process, and how to adopt an automated governance ...
Natural Language Processing Projects
Natural Language Processing Projects

Leverage machine learning and deep learning techniques to build fully-fledged natural language processing (NLP) projects. Projects throughout this book grow in complexity and showcase methodologies, optimizing tips, and tricks to solve various business problems. You will use modern Python libraries and algorithms to build end-to-end NLP projects. The book starts with an overview of natural language processing (NLP) and artificial intelligence to provide a quick refresher on algorithms. Next, it covers end-to-end NLP projects beginning with traditional algorithms and projects such as customer review sentiment and emotion detection, topic modeling, and document clustering. From there, it delves into e-commerce related projects such as product categorization using the description of the product, a search engine to retrieve the relevant content, and a content-based recommendation system to enhance user experience. Moving forward, it explains how to build systems to find similar sentenc ...
Microsoft Excel Pivot Table Data Crunching
Microsoft Excel Pivot Table Data Crunching

Use Microsoft 365 Excel and Excel 2021 pivot tables and pivot charts to produce powerful, dynamic reports in minutes: take control of your data and your business! Even if youve never created a pivot table before, this book will help you leverage all their flexibility and analytical power including important recent improvements in Microsoft 365 Excel. Drawing on more than 30 years of cutting-edge Excel experience, MVP Bill Jelen (MrExcel) shares practical recipes for solving real business problems, expert insights for avoiding mistakes, and advanced tips and tricks youll find nowhere else. By reading this book, you will: Master easy, powerful ways to create, customize, change, and control pivot tables; Transform huge datasets into clear summary reports; Instantly highlight your most profitable customers, products, or regions; Use the data model and Power Query to quickly analyze disparate data sources; Create powerful crosstab reports with new dynamic arrays and Power Query; Build ge ...
Software Architecture with C# 10 and .NET 6, 3rd Edition
Software Architecture with C# 10 and .NET 6, 3rd Edition

Software architecture is the practice of implementing structures and systems that streamline the software development process and improve the quality of an app. This fully revised and expanded third edition, featuring the latest features of .NET 6 and C# 10, enables you to acquire the key skills, knowledge, and best practices required to become an effective software architect. Software Architecture with C# 10 and .NET 6, Third Edition features new chapters that describe the importance of the software architect, microservices with ASP.NET Core, and analyzing the architectural aspects of the front-end in the applications, including the new approach of .NET MAUI. It also includes a new chapter focused on providing a short introduction to artificial intelligence and machine learning using ML.NET, and updated chapters on Azure Kubernetes Service, EF Core, and Blazor. You will begin by understanding how to transform user requirements into architectural needs and exploring the differenc ...
Transformers for Natural Language Processing, 2nd Edition
Transformers for Natural Language Processing, 2nd Edition

Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence. Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question-answering, and many more NLP domains with transformers. An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP. This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers ...
Practical AI for Healthcare Professionals
Practical AI for Healthcare Professionals

Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However, notions of what AI actually is and how it works are often not discussed. Furthermore, information on AI implementation is often tailored towards seasoned programmers rather than the healthcare professional/beginner coder. This book gives an introduction to practical AI in the medical sphere, focusing on real-life clinical problems, how to solve them with actual code, and how to evaluate the efficacy of those solutions. You'll start by learning how to diagnose problems as ones that can and cannot be solved with AI. You'll then learn the basics of computer science algorithms, neural networks, and when each should be applied. Then you'll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The Tensorflow/Keras library along with Numpy and Scikit-Learn are covered as well. Once you've mastered those basic computer science and programming concepts, you ...
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