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  • Book Overview & Buying AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide
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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide

AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition

By : Somanath Nanda, Weslley Moura
4.6 (22)
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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide

AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide

4.6 (22)
By: Somanath Nanda, Weslley Moura

Overview of this book

The AWS Certified Machine Learning Specialty (MLS-C01) exam evaluates your ability to execute machine learning tasks on AWS infrastructure. This comprehensive book aligns with the latest exam syllabus, offering practical examples to support your real-world machine learning projects on AWS. Additionally, you'll get lifetime access to supplementary online resources, including mock exams with exam-like timers, detailed solutions, interactive flashcards, and invaluable exam tips, all accessible across various devices—PCs, tablets, and smartphones. Throughout the book, you’ll learn data preparation techniques for machine learning, covering diverse methods for data manipulation and transformation across different variable types. Addressing challenges such as missing data and outliers, the book guides you through an array of machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, accompanied by requisite machine learning algorithms essential for exam success. The book helps you master the deployment of models in production environments and their subsequent monitoring. Equipped with insights from this book and the accompanying mock exams, you'll be fully prepared to achieve the AWS MLS-C01 certification.
Table of Contents (13 chapters)
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Implementing natural language processing with Amazon Comprehend

This service helps you extract insights from unstructured text. Unstructured text information is growing exponentially. A few data source examples are as follows:

  • Customer engagement: Call center, issue triage, customer surveys, and product reviews
  • Business processes: Customer/vendor emails, product support messages, and operation support feedback
  • Records and research: Whitepapers and medical records
  • News and social media: Social media analytics, brand trends, and correlated events

Now, the question is, what can you do with this data? How can you analyze it and extract any value out of it? The answer is Amazon Comprehend, which is used to get insights from unstructured data.

Some common uses of Amazon Comprehend include the following:

  • Information management system
  • More accurate search system on organized topics
  • Sentiment analysis of users
  • Support ticket classification
  • ...
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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide
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