AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide
Language: English
Published by Packt Publishing, 2024
- Softcover
- New
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- Title
- AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide
- Author
- Somanath Nanda
- Publisher
- Packt Publishing
- Publication year
- 2024
- Condition
- New
- Binding
- PAP
- Language
- English
- ISBN 10
- 1835082203
- ISBN 13
- 9781835082201
- Edition
- 2nd Edition
- Item weight
- 640 grams
Prepare confidently for the AWS MLS-C01 certification with this comprehensive and up-to-date exam guide, accompanied by web-based tools such as mock exams, flashcards, and exam tips
Key Features
- Gain proficiency in AWS machine learning services to excel in the MLS-C01 exam
- Build model training and inference pipelines and deploy machine learning models to the AWS cloud
- Practice on the go with the mobile-friendly bonus website, accessible with the book
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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.
What you will learn
- Identify ML frameworks for specific tasks
- Apply CRISP-DM to build ML pipelines
- Combine AWS services to build AI/ML solutions
- Apply various techniques to transform your data, such as one-hot encoding, binary encoder, ordinal encoding, binning, and text transformations
- Visualize relationships, comparisons, compositions, and distributions in the data
- Use data preparation techniques and AWS services for batch and real-time data processing
- Create training and inference ML pipelines with Sage Maker
- Deploy ML models in a production environment efficiently
Who this book is for
This book is designed for both students and professionals preparing for the AWS Certified Machine Learning Specialty exam or enhance their understanding of machine learning, with a specific emphasis on AWS. Familiarity with machine learning basics and AWS services is recommended to fully benefit from this book.
Table of Contents
- Machine Learning Fundamentals
- AWS Services for Data Storage
- AWS Services for Data Migration and Processing
- Data Preparation and Transformation
- Data Understanding and Visualization
- Applying Machine Learning Algorithms
- Evaluating and Optimizing Models
- AWS Application Services for AI/ML
- Amazon SageMaker Modeling
- Model Deployment
"Synopsis" may belong to another edition of this title.
About the Author
Somanath has 10 years of working experience in IT industry which includes Prod development, Devops, Design and architect products from end to end. He has also worked at AWS as a Big Data Engineer for about 2 years.
Weslley Moura has been developing data products for the past decade. At his recent roles, he has been influencing data strategy and leading data teams into the urban logistics and blockchain industries.
"About the title" may belong to another edition of this title.
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