IT Professional Curricula     Internet and Network Technologies Solution Area     Cloud Computing     AWS Certified Machine Learning

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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.

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Objectives

AWS Certified Machine Learning: Problem Formulation & Data Collection 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