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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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Unsupervised learning

AWS provides several unsupervised learning algorithms for the following tasks:

  • Clustering: K-Means algorithm
  • Dimension reduction: Principal Component Analysis (PCA)
  • Pattern recognition: IP Insights
  • Anomaly detection: The Random Cut Forest (RCF) algorithm

Let us start by talking about clustering and how the most popular clustering algorithm works: K-Means.

Clustering

Clustering algorithms are very popular in data science. Basically, they aim to identify similar groups in a given dataset, also known as clusters. Clustering algorithms belong to the field of non-supervised learning, which means that they do not need a label or response variable to be trained.

This is just fantastic since labeled data is very scarce! However, it comes with some limitations. The main one is that clustering algorithms provide clusters for you, but not the meaning of each cluster. Thus, someone, as a subject matter expert, has to analyze the properties...

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