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Applied Machine Learning for Healthcare and Life Sciences using AWS

Applied Machine Learning for Healthcare and Life Sciences using AWS

By : Ujjwal Ratan
4.9 (14)
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Applied Machine Learning for Healthcare and Life Sciences using AWS

Applied Machine Learning for Healthcare and Life Sciences using AWS

4.9 (14)
By: Ujjwal Ratan

Overview of this book

While machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics. This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You’ll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications. By the end of this book, you’ll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence.
Table of Contents (19 chapters)
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1
Part 1: Introduction to Machine Learning on AWS
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Chapter 1: Introducing Machine Learning and the AWS Machine Learning Stack
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Part 2: Machine Learning Applications in the Healthcare Industry
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Part 3: Machine Learning Applications in the Life Sciences Industry
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Part 4: Challenges and the Future of AI in Healthcare and Life Sciences
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Index
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Summary

In this chapter, you got an overview of the basic concepts of ML. You went over the definition of ML and how it differs from typical software. You also learned about important terminologies and concepts that are heavily used in the context of ML. The chapter also covered the important steps of the ML life cycle, which can be combined together to create an end-to-end ML pipeline to deploy models in production. Lastly, you got an introduction to the AWS ML stack and how the AWS AI/ML services are organized.

In Chapter 2, Exploring Key AWS Machine Learning Services for Healthcare and Life Sciences, we will dive into the details of some of the critical AWS services that allow healthcare and life sciences customers to build, train, and deploy ML models for solving important problems in the industry. We will cover those problems in detail in the subsequent chapters of this book.

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