Actions for 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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AWS Certified Machine Learning Specialty (MLS-C01) certification guide : the ultimate guide to passing the MLS-C01 exam on your first attempt / Somanath Nanda, Weslley Moura

Author
Nanda, Somanath
Published
Birmingham, UK : Packt Publishing Ltd., 2024.
Edition
Second edition.
Physical Description
1 online resource (342 pages) : illustrations
Additional Creators
Moura, Weslley
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Contents
Cover -- FM -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: Machine Learning Fundamentals -- Making the Most Out of this Book -- Your Certification and Beyond -- Comparing AI, ML, and DL -- Examining ML -- Examining DL -- Classifying supervised, unsupervised, and reinforcement learning -- Introducing supervised learning -- The CRISP-DM modeling life cycle -- Data splitting -- Overfitting and underfitting -- Applying cross-validation and measuring overfitting -- Bootstrapping methods -- The variance versus bias trade-off -- Shuffling your training set, Modeling expectations -- Introducing ML frameworks -- ML in the cloud -- Summary -- Exam Readiness Drill -- Chapter Review Questions -- Chapter 2: AWS Services for Data Storage -- Technical requirements -- Storing Data on Amazon S3 -- Creating buckets to hold data -- Distinguishing between object tags and object metadata -- Controlling access to buckets and objects on amazon s3 -- S3 bucket policy -- Protecting data on amazon s3 -- Applying bucket versioning -- Applying encryption to buckets -- Securing s3 objects at rest and in transit -- Using other types of data stores, Relational Database Service (RDS) -- Managing failover in Amazon RDS -- Taking automatic backups, RDS snapshots, and restore and read replicas -- Writing to Amazon Aurora with multi-master capabilities -- Storing columnar data on Amazon Redshift -- Amazon DynamoDB for NoSQL Database-as-a-Service -- Summary -- Exam Readiness Drill -- Chapter Review Questions -- Chapter 3: AWS Services for Data Migration and Processing -- Technical requirements -- Creating ETL jobs on AWS Glue -- Features of AWS Glue -- Getting hands-on with AWS Glue Data Catalog components, Getting hands-on with AWS Glue ETL components -- Querying S3 data using Athena -- Processing real-time data using Kinesis Data Streams -- Storing and transforming real-time data using Kinesis Data Firehose -- Different ways of ingesting data from on-premises into AWS -- AWS Storage Gateway -- Snowball, Snowball Edge, and Snowmobile -- AWS DataSync -- AWS Database Migration Service -- Processing stored data on AWS -- AWS EMR -- AWS Batch -- Summary -- Exam Readiness Drill -- Chapter Review Questions -- Chapter 4: Data Preparation and Transformation -- Identifying types of features, Dealing with categorical features -- Transforming nominal features -- Applying binary encoding -- Transforming ordinal features -- Avoiding confusion in our train and test datasets -- Dealing with numerical features -- Data normalization -- Data standardization -- Applying binning and discretization -- Applying other types of numerical transformations -- Understanding data distributions -- Handling missing values -- Dealing with outliers -- Dealing with unbalanced datasets -- Dealing with text data -- Bag of words -- TF-IDF -- Word embedding -- Summary, and Exam Readiness Drill -- Chapter Review Questions
Summary
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 hands-on activities 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.
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Genre(s)
ISBN
9781835082201
View MARC record | catkey: 43941027