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

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.

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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 tipsKey FeaturesGain proficiency in AWS machine learning services to excel in the MLS-C01 examBuild model training and inference pipelines and deploy machine learning models to the AWS cloudPractice on the go with the mobile-friendly bonus website, accessible with the bookPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe 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 learnIdentify ML frameworks for specific tasksApply CRISP-DM to build ML pipelinesCombine AWS services to build AI/ML solutionsApply various techniques to transform your data, such as one-hot encoding, binary encoder, ordinal encoding, binning, and text transformationsVisualize relationships, comparisons, compositions, and distributions in the dataUse data preparation techniques and AWS services for batch and real-time data processingCreate training and inference ML pipelines with Sage MakerDeploy ML models in a production environment efficientlyWho this book is forThis 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.