Certification     Amazon     AWS Certified Machine Learning – Specialty     AWS Certified Machine Learning – Specialty

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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.

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Objectives

AWS Certified Machine Learning: Problem Framing & Algorithm Selection 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