Exam Readiness: AWS Certified Machine Learning - Specialty

Languages Available: Deutsch | Español (Latinoamérica) | Français | Bahasa Indonesia | Italiano | 日本語 | 한국어 | Português (Brasil) | 中文（简体) | 中文（繁體) | Tiếng ViệtThis course prepares you to take the AWS Certified Machine Learning – Specialty exam, which validates your ability to design, implement, deploy, and maintain machine learning (ML) solutions. In this course, you’ll learn about the logistics of the exam and the mechanics of exam questions, and you’ll explore the exam’s technical domains. You’ll review core AWS services and key concepts for the exam domains:Data EngineeringExploratory Data AnalysisModelingMachine Learning Implementation and OperationsYou’ll also learn key test-taking strategies and will put them into action, taking multiple study questions. Once you’ve honed your skills, you’ll have the chance to take a quiz that will help you assess your areas of strength and weakness, so that you’ll know what to emphasize in your pre-exam studies.Course objectivesBy the end of this course, you will be able to:Identify your strengths and weaknesses in each exam domain so that you know what to focus on when studying for the examDescribe the technical topics and concepts that make up each of the exam domainsSummarize the logistics and mechanics of the exam and its questionsUse effective strategies for studying and taking the exam Intended audienceThis course is intended for:ML practitioners who have at least one year of practical experience, and who are preparing to take the AWS Certified Machine Learning – Specialty examPrerequisitesWe recommend that attendees of this course have:Proficiency expressing the intuition behind basic ML algorithms and performing basic hyperparameter optimizationUnderstanding of the ML pipeline and its componentsExperience with ML and deep learning frameworksUnderstanding of and experience in model training, deployment, and operational best practicesEnrollCourse outlineModule 0: Course IntroductionModule 1: Exam Overview and Test-taking StrategiesExam overview, logistics, scoring, and user interfaceQuestion mechanics and designTest-taking strategiesModule 2: Domain 1 - Data EngineeringDomain 1.1: Data Repositories for MLDomain 1.2: Identify and implement a data-ingestion solutionDomain 1.3: Identify and implement a data-transformation solutionWalkthrough of study questionsDomain 1 quizModule 3: Domain 2 - Exploratory Data AnalysisDomain 2.1: Sanitize and prepare data for modeling Domain 2.2: Perform featuring engineeringDomain 2.3: Analyze and visualize data for MLWalkthrough of study questionsDomain 2 quizModule 4: Domain 3 - ModelingDomain 3.1: Frame business problems as ML problemsDomain 3.2: Select the appropriate model(s) for a given ML problemDomain 3.3: Train ML modelsDomain 3.4 Perform hyperparameter optimizationDomain 3.5 Evaluate ML modelsWalkthrough of study questionsDomain 3 quizModule 5: Domain 4 - ML Implementation and OperationsDomain 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault toleranceDomain 4.2: Recommend and implement the appropriate ML services and features for a given problemDomain 4.3: Apply basic AWS security practices to ML solutionsDomain 4.4: Deploy and operationalize ML solutionsWalkthrough of study questionsDomain 4 quizModule 6: Additional Study QuestionsOpportunity to take additional study questionsModule 7: Recommended Study MaterialLinks to AWS blogs, documentation, FAQs, and other recommended study material for the examModule 8: Course Wrap-upHow to sign up for the examCourse summaryCourse feedback