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In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem
C# 7.1 and .NET Core 2.0 – Modern Cross-Platform Development, Third Edition, is a practical guide to creating powerful cross-platform applications with C# 7.1 and .NET Core 2.0. It gives readers of any experience level a solid foundation in C# and .NET. The first part of the book runs you through the basics of C#, as well as debugging functions and object-oriented programming, before taking a quick tour through the latest features of C# 7.1 such as default literals, tuples, inferred tuple names, pattern matching, out variables, and more.After quickly taking you through C# and how .NET works, this book dives into the .NET Standard 2.0 class libraries, covering topics such as packaging and deploying your own libraries, and using common libraries for working with collections, performance, monitoring, serialization, files, databases, and encryption. The final section of the book demonstrates the major types of application that you can build and deploy cross-device and cross-platform. In this section, you'll learn about websites, web applications, web services, Universal Windows Platform (UWP) apps, and mobile apps. By the end of the book, you'll be armed with all the knowledge you need to build modern, cross-platform applications using C# and .NET.
When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one.You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch.Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. And finally, you'll get acquainted with AWS AI services and their applications in real-world use cases.By the end of this audiobook, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.
Cybercriminals are incessantly coming up with new ways to compromise online systems and wreak havoc, creating an ever-growing need for cybersecurity practitioners in every organization across the globe who understand international security standards, such as the ISO27k family of standards.If you’re looking to ensure that your company's data conforms to these standards, Cybersecurity and Privacy Law Handbook has got you covered. It'll not only equip you with the rudiments of cybersecurity but also guide you through privacy laws and explain how you can ensure compliance to protect yourself from cybercrime and avoid the hefty fines imposed for non-compliance with standards.Assuming that you're new to the field, this book starts by introducing cybersecurity frameworks and concepts used throughout the chapters. You'll understand why privacy is paramount and how to find the security gaps in your company's systems. There's a practical element to the book as well—you'll prepare policies and procedures to prevent your company from being breached. You’ll complete your learning journey by exploring cloud security and the complex nature of privacy laws in the US.By the end of this cybersecurity book, you'll be well-placed to protect your company's data and comply with the relevant standards.
Extending Dynamics 365 Customer Engagement Apps with Low Code helps you gain a comprehensive, practical understanding of how a no-code/low-code project approach works for Dynamics 365 (D365) Customer Engagement (CE). This book covers the most relevant native capabilities for configuration, along with real-world scenarios to showcase the magic of extending D365 CE apps with Power Platform.The book starts by identifying different scenarios and use cases to extend D365 CE apps with a low-code approach. You’ll learn about the different capabilities of Dataverse and Power Apps used to extend native applications. Next, you'll discover how to leverage Power Apps, both Canvas apps and model-driven apps, and Power Pages, to build apps and portals around D365 CE processes. You’ll also explore Power Automate's capabilities to create or modify business processes, as well as incorporate new processes and automation. As you advance, you’ll also discover how Power Virtual Agents can be implemented in D365 CE apps. The book concludes by teaching you to integrate Power BI natively with customer engagement, thus facilitating the construction of advanced reports and dashboards.By the end of this book, you’ll have gained hands-on expertise in customizing CE apps with Power Platform to deliver more scalable and maintainable solutions.