AWS-MLPDSS - Practical Data Science with Amazon SageMaker

In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.

Student Testimonials

Instructor did a great job, from experience this subject can be a bit dry to teach but he was able to keep it very engaging and made it much easier to focus. Student
Excellent presentation skills, subject matter knowledge, and command of the environment. Student
Instructor was outstanding. Knowledgeable, presented well, and class timing was perfect. Student

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Prerequisites


We recommend that attendees of this course have the following prerequisites:
Working knowledge of a programming language

Detailed Class Syllabus


Outline:


Business problem: Churn prediction
Load and display the dataset
Assess features and determine which Amazon SageMaker algorithm to use
Use Amazon Sagemaker to train, evaluate, and automatically tune the model
Deploy the model
Assess relative cost of errors