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 a