[MLS-C01] [Introduction] Course Outline

Outline of the course

Posted by Oscaner on May 1, 2022


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


  • 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


  • 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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