AWS - Practical Data Science with Amazon SageMaker
You will learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use case includes customer retention analysis to inform customer
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Description
In this course, you will:
Prepare a dataset for training
Train and evaluate a Machine Learning model
Automatically tune a Machine Learning model
Prepare a Machine Learning model for production
Think critically about Machine Learning model results
