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

You're reading from   AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide The ultimate guide to passing the MLS-C01 exam on your first attempt

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781835082201
Length 342 pages
Edition 2nd Edition
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Authors (2):
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Somanath Nanda Somanath Nanda
Author Profile Icon Somanath Nanda
Somanath Nanda
Weslley Moura Weslley Moura
Author Profile Icon Weslley Moura
Weslley Moura
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Machine Learning Fundamentals FREE CHAPTER 2. Chapter 2: AWS Services for Data Storage 3. Chapter 3: AWS Services for Data Migration and Processing 4. Chapter 4: Data Preparation and Transformation 5. Chapter 5: Data Understanding and Visualization 6. Chapter 6: Applying Machine Learning Algorithms 7. Chapter 7: Evaluating and Optimizing Models 8. Chapter 8: AWS Application Services for AI/ML 9. Chapter 9: Amazon SageMaker Modeling 10. Chapter 10: Model Deployment 11. Chapter 11: Accessing the Online Practice Resources 12. Other Books You May Enjoy

Summary

That was such a journey! Take a moment to recap what you have just learned. This chapter had four main topics: supervised learning, unsupervised learning, textual analysis, and image processing. Everything that you have learned fits into those subfields of machine learning.

The list of supervised learning algorithms that you have studied includes the following:

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

Remember that you can use linear learner, factorization machines, XGBoost, and KNN for multiple purposes, including solving regression and classification problems. Linear learner is probably the simplest algorithm out of these four; factorization machines extends linear earner and is good for sparse datasets, XGBoost uses an ensemble method based on decision trees, and KNN is an index-based algorithm.

The other two algorithms, Object2Vec and DeepAR, are used for specific purposes. Object2Vec is used...

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