AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to: - Prioritize the most promising directions for an AI project - Diagnose errors in a machine learning system - Build ML in complex settings, such as mismatched training/ test sets - Set up an ML project to compare to and/or surpass human- level performance - Know when and how to apply end-to-end learning, transfer learning, and multi-task learning.

Discover how to build predictive AI models for manufacturing processes, optimizing efficiency, productivity and decision-making.

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This article explores how you can use ML.NET to build fully-fledged machine learning models quickly and effectively.

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Model Registry in MLOps offers a collaborative hub to work together on various stages of the ML lifecycle.

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This Domain is about the production environment and related features to make everything work. It comprises 20% of the exam marks. There are four sub-domains.

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Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Define your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy and monitor your models in a production environment Product DetailsISBN-13: 9781492045113 Media Type: Paperback Publisher: O'Reilly Media - Incorporated Publication Date: 02-11-2020 Pages: 257 Product Dimensions: 6.90(w) x 9.10(h) x 0.60(d)About the Author Emmanuel Ameisen has worked for years as a Data Scientist. He implemented and deployed predictive analytics and machine learning solutions for Local Motion and Zipcar. Recently, Emmanuel has led Insight Data Science's AI program where he oversaw more than a hundred machine learning projects. Emmanuel holds graduate degrees in artificial intelligence, computer engineering, and management from three of France’s top schools.

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