Google Cloud Machine Learning Engineer Certification Prep

Machine Learning Engineer is a rewarding, in demand role, and increasingly important to organizations moving building data intensive services in the cloud.  The Google Cloud Professional Machine Learning Engineer certification is one of the field's most recognized credentials. This course will help prepare you to take and pass the exam.  Specifically, this course will help you understand the details of:Building and deploying ML models to solve business challenges using Google Cloud services and best practices for machine learning Aspects of machine learning model architecture, data pipelines structures, optimization, as well as monitoring model performance in productionFundamental concepts of model development, infrastructure management, data engineering, and data governancePreparing data, optimizing storage formats, performing exploratory data analysis, and handling missing dataFeature engineering, data augmentation, and feature encoding to maximize the likelihood of building successful modelsUnderstand responsible AI throughout the ML development process and apply proper controls and governance to ensure fairness in machine learning models. By the end of this course, you will know how to use Google Cloud services for machine learning and just as importantly, you will understand machine learning concepts and techniques needed to use those services effectively.Unlike courses that set out to teach you how to use particular Google Cloud services, this course is designed to teach you services as well as all the topics covered in the Google Cloud Professional Machine Learning Exam Guide, including machine learning fundamentals and techniques. The course begins with a discussion of framing business problems as machine learning problems followed by a chapter on the technical framing on ML problems.  We next review the a