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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 : Nanda, 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: Nanda, 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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Handling missing values

As the name suggests, missing values refer to the absence of data. Such absences are usually represented by tokens, which may or may not be implemented in a standard way.

Although using tokens is standard, the way those tokens are displayed may vary across different platforms. For example, relational databases represent missing data with NULL, core Python code will use None, and some Python libraries will represent missing numbers as Not a Number (NaN).

Important note

For numerical fields, don’t replace those standard missing tokens with zeros. By default, zero is not a missing value, but another number.

However, in real business scenarios, you may or may not find those standard tokens. For example, a software engineering team might have designed the system to automatically fill missing data with specific tokens, such as “unknown” for strings or “-1” for numbers. In that case, you would have to search by those two...

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