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apply Automated Machine Learning, a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar;
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Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using;
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science and discusses the statistical techniques data scientists should know. You'll then move on to machine learning in Azure where you will;
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ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML;
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and discusses the statistical techniques data scientists should know. You'll then move on to machine learning in Azure where you will;
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Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data;
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programmers how to build and deploy scalable machine learning and deep learning models using serverless architectures with Microsoft Azure. You'll;
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science or software engineering experience. About the Exam Exam AI-900 focuses on knowledge needed to identify;
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business solutions. It also walks you through how to build powerful machine learning models using Azure Data Lake, Databricks, Azure Synapse;
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Create applications using Industry 4.0. Discover how artificial intelligence (AI) and machine learning (ML) capabilities can be enhanced;
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Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key;
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Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks Key Features Get to grips with the;
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Learning services. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision;
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Learn to extract actionable insights from your big data in real time using a range of Microsoft Azure features Key Features Updated;
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, and when they finish they'll be well on the way to Azure mastery! Key Features * Creating virtual machines and web apps;
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want to explore and learn more about deep learning and implement it using the Microsoft AI platform;
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expertise to build cloud-native solutions using Microsoft Azure and .NET. The book starts with essential topics that will help get you familiar with;
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Design, build, and justify an optimal Microsoft IoT footprint to meet your project needs. This book describes common Internet of Things;
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is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages;
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. Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R;
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data. As you advance, you'll learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts. By;
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* Learn the fundamentals of deep learning, including neural network design * Leverage AI cloud services to build better real-world;
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machine learning and AI tools in Azure, including Azure Cognitive Services. What You Will Learn Apply design guidance and best practices using;
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machine learning and AI tools in Azure, including Azure Cognitive Services. What You Will Learn Apply design guidance and best practices using;
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. The book also covers ethical considerations that are becoming increasingly of concern when using AI to drive decision making. You will be;
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Implement machine learning and deep learning techniques to perform predictive analytics on real-time IoT data Key Features Discover;
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learning, and data science, and also includes case studies of well-known AI systems. The second is a step-by-step introduction to Azure Machine;
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