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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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Data Preparation and Transformation

You have probably heard that data scientists spend most of their time working on data-preparation-related activities. It is now time to explain why that happens and what types of activities they work on.

In this chapter, you will learn how to deal with categorical and numerical features, as well as how to apply different techniques to transform your data, such as one-hot encoding, binary encoders, ordinal encoding, binning, and text transformations. You will also learn how to handle missing values and outliers in your data, which are two important tasks you can implement to build good machine learning (ML) models.

In this chapter, you will cover the following topics:

  • Identifying types of features
  • Dealing with categorical features
  • Dealing with numerical features
  • Understanding data distributions
  • Handling missing values
  • Dealing with outliers
  • Dealing with unbalanced datasets
  • Dealing with text data

This...

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