Practical Data Science with Amazon SageMaker (PDSASM)

In this intermediate-level course, you will learn how to solve a real-world use case with machine learning (ML) and produce actionable results using Amazon SageMaker. The 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. A real life use case includes customer retention analysis to inform customer loyalty programs.

• Developers
• Data Scientists

This course is part of the following Certifications:

• AWS Certified Machine Learning - Specialty

• Familiarity with Python programming language
• Basic understanding of Machine Learning

• 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

1 Day

In this intermediate-level course, individuals 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 cases include customer retention analysis to inform customer loyalty programs.