Outline
Data Engineering
- Basic data engineering concepts and terminology
- Creating data repositories for machine learning
- Identifying and implementing data-ingestion and data-transformation solutions
- AWS data migration services and tools
Data Analysis
- Basic data analysis concepts and terminology
- Kinesis Data Streams
- Kinesis Data Firehose
- Kinesis Video Streams
- Kinesis Data Analytics
- Sanitize and prepare data for modeling
- Feature engineering
Modeling
- Select the appropriate model(s) for a given machine learning problem
- Train machine learning models
- Tune and optimize hyperparameters
- Evaluate effectiveness of machine learning models
Algorithm
- Basic algorithm concepts
- Types of algorithms
- Regression
- Clustering
- Classification
- Image Analysis
- Anomaly Detection
- Text Analysis
- Reinforcement Learning
- Forecasting
Implementation and Operations
- Modeling concepts
- Building machine learning solutions that are performant available scalable resilient and fault tolerant
- Recommending and implementing the appropriate machine learning services and features for a given problem
- Applying AWS security best practices to your machine learning solutions
Course Summary and Exam Tips
- Course summary
- Exam day details and tips
- Section quiz
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