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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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Supervised learning

AWS provides supervised learning algorithms for general purposes (regression and classification tasks) and more specific purposes (forecasting and vectorization). The list of built-in algorithms that can be found in these sub-categories is as follows:

  • Linear learner algorithm
  • Factorization machines algorithm
  • XGBoost algorithm
  • KNN algorithm
  • Object2Vec algorithm
  • DeepAR forecasting algorithm

You will start by learning about regression models and the linear learner algorithm.

Working with regression models

Looking at linear regression models is a nice way to understand what is going on inside regression models in general (linear and non-linear regression models). This is mandatory knowledge for every data scientist and can help you solve real challenges as well. You will now take a closer look at this in the following subsections.

Introducing regression algorithms

Linear regression models aim to predict a numeric value...

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