Detailed Course Outline
Day One
Module 0: Course Introduction
Module 1: Exam Overview and Test-taking Strategies
- Exam overview, logistics, scoring, and user interface
- Question mechanics and design
- Test-taking strategies
Module 2: Domain 1: Data Engineering
- Domain 1.1: Data Repositories for machine learning
- Domain 1.2: Identify and implement a data-ingestion solution
- Domain 1.3: Identify and implement a data-transformation solution
- Walkthrough of study questions
- Domain 1 quiz
Module 3: Domain 2: Exploratory Data Analysis
- Domain 2.1: Sanitize and prepare data for modeling
- Domain 2.2: Perform featuring engineering
- Domain 2.3: Analyze and visualize data for ML
- Walkthrough of study questions
- Domain 2 quiz
Module 4: Domain 3: Modeling
- Domain 3.1: Frame business problems as machine learning (ML) problems
- Domain 3.2: Select the appropriate model(s) for a given ML problem
- Domain 3.3: Train ML models
- Domain 3.4 Perform hyperparameter optimization
- Domain 3.5 Evaluate ML models
- Walkthrough of study questions
- Domain 3 quiz
Module 5: ML Implementation and Operations
- Domain 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance
- Domain 4.2: Recommend and implement the appropriate ML services and features for a given problem
- Domain 4.3: Apply basic AWS security practices to ML solutions
- Domain 4.4: Deploy and operationalize ML solutions
- Walkthrough of study questions
- Domain 4 quiz
Module 6: Comprehensive study questions
Module 7: Study Material
Module 8: Wrap-up