The AWS Machine Learning Specialty certification exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus in depth using practical examples to help you with your real-world machine learning projects on AWS.
Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover different techniques for data manipulation and transformation for different types of variables. The book also covers the handling of missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with their specific ML algorithms, that you should know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them.
By the end of the book, you'll have gained knowledge of all the key fields of machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS machine learning.