Practical Data Science with Amazon SageMaker
In this course, you’ll learn how to solve a real-world use case with machine learning (ML) and produce real-world results using Amazon SageMaker. The course walks through the steps of a typical machine learning data science process, from analyzing and visualizing a dataset to data preparation and feature engineering. Participants will also learn the practicalities of building, training, tuning, and deploying models with Amazon SageMaker. A real use case includes customer retention analytics to inform loyalty programs.
COD: AW-PDSASM
Categorie: AWS
DESCRIPTION
COURSE OBJECTIVES
COURSE CONTENT
ADDITIONAL INFORMATION
DESCRIPTION
Who should participate
- Developers
- Data Scientists
Prerequisites
- Familiarity with the Python programming language
- Basic knowledge of Machine Learning
COURSE OBJECTIVES
In this course you will learn to:
- 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
- Reflect critically on the results of the machine learning model
COURSE CONTENT
- 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
ADDITIONAL INFORMATION
Duration – 1 day
Delivery – in Classroom, On Site, Remote
PC and SW requirements:
- Internet connection
- Web browser, Google Chrome
- Zoom
Language
Instructor: English
Workshop: English
Slides: English
