AWS Certified Machine Learning: Problem Framing & Algorithm Selection

Amazon Web Services 2021    |    Intermediate
  • 12 Videos | 1h 12m 30s
  • Includes Assessment
  • Earns a Badge
Problem framing and algorithm selection is the most important part of any machine learning (ML) project. ML engineers have to apply appropriate techniques that will result in expected prediction behavior. It is important to fully understand a particular task and choose among all the available methods and toolkits before implementing a machine learning project. Use this course to learn more about the ML mindset, discover how goal-oriented business problems can be formulated as machine learning problems, and describe factors that drive the selection of the correct algorithm for a particular scenario. The course will also help you refresh important ML concepts and terminologies. After completing this course, you'll be able to implement machine learning solutions to solve business problems, further preparing you for the AWS Certified Machine Learning – Specialty certification exam.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    outline machine learning (ML) mindset and compare the ML approach to other problem-solving techniques
    define the key characteristics of good machine learning problems
    describe the most challenging problems in machine learning (ML)
    specify how to clearly define a business problem and set success and failure criteria
    describe how to design a good output for a business problem
  • identify how to formulate a business problem into a machine learning problem
    define the importance of the availability of good data and data pipeline design
    evaluate the learning ability of a machine learning model and identify potential risks and biases in the dataset as well as their resulting impact
    specify the factors that impact algorithm selection for a particular use case
    review core machine learning concepts covered in the AWS examination, such as confusion matrices, precision, and recall
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 41s
    UP NEXT
  • Playable
    2. 
    Machine Learning Mindset and Project Life Cycle
    5m 54s
  • Locked
    3. 
    Machine Learning (ML) Solvable Problems
    9m 24s
  • Locked
    4. 
    Difficult Problems in Machine Learning
    4m 52s
  • Locked
    5. 
    Identifying Machine Learning Use Cases and Metrics
    5m 44s
  • Locked
    6. 
    Identifying Expected Outcome
    3m 53s
  • Locked
    7. 
    Formulating Machine Learning Questions
    5m 59s
  • Locked
    8. 
    Data Sources and Data Preparation for ML
    5m 59s
  • Locked
    9. 
    Learning Ability and Potential Bias
    7m 27s
  • Locked
    10. 
    Considerations for Algorithm Selection
    6m 21s
  • Locked
    11. 
    Machine Learning Refresher
    8m 11s
  • Locked
    12. 
    Course Summary
    1m 6s

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