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.
At Course Completion
Using Amazon SageMaker, this course teaches you how 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.
- Think critically about machine learning model results.
This course is intended for:
- A technical audience at an intermediate level
We recommend that attendees of this course have the following prerequisites:
- Working knowledge of a programming language
- 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