AWS Certified Machine Learning: Problem Formulation & Data Collection

Amazon Web Services 2021    |    Intermediate
  • 12 Videos | 42m 49s
  • Includes Assessment
  • Earns a Badge
In order to build machine learning (ML) applications, it is important to formulate problems and collect data. Examine the choice between the online and on-premise implementation of the problem formulation and data collection phases through this course. Explore how SageMaker algorithms help complete ML projects efficiently and work with various approaches that implement recommender systems. You'll also investigate how and when to use AWS data storage services and learn more about analyzing dataset readiness. After taking this course, you'll be able to describe the advantages and disadvantages of using the cloud over an on-premise solution and define the problem formulation and success evaluation processes. You'll also be a step closer to preparing for the AWS Certified Machine Learning – Specialty certification exam.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    describe scenarios where using the cloud is preferential to an on-premise solution and list the main types of problems that can be solved using machine learning
    describe the problem formulation process and define the success metrics for a machine learning project
    list examples of business problem formulation relating to machine learning
    outline Amazon's real-life problem formulation practice for commercial use of recommender systems
    describe the theoretical concepts behind recommender systems
  • identify the advantages and disadvantages of collaborative filtering
    distinguish between various AWS data storage services
    work with S3 buckets to read a dataset using Python and SageMaker
    perform data quality checks on Amazon Reviews dataset using Python and SageMaker
    enumerate several built-in SageMaker algorithms
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    59s
    UP NEXT
  • Playable
    2. 
    Applications of Machine Learning Solutions
    5m 43s
  • Locked
    3. 
    Business Problem and Success Evaluation Metrics
    3m 51s
  • Locked
    4. 
    Problem Formulation
    4m 32s
  • Locked
    5. 
    Amazon Review Problem Formulation
    3m 15s
  • Locked
    6. 
    Recommender Systems
    3m 42s
  • Locked
    7. 
    Recommender Systems Collaborative Filtering
    3m 26s
  • Locked
    8. 
    Storage Services on AWS: EBS, EFS, and S3
    3m 35s
  • Locked
    9. 
    Reading Data from Amazon S3
    3m 35s
  • Locked
    10. 
    Analyzing Data Readiness and Appropriateness
    2m 11s
  • Locked
    11. 
    Built-in Algorithms in Amazon SageMaker
    2m 30s
  • Locked
    12. 
    Course Summary
    30s

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