Practical Data Science with Amazon SageMaker
Learn how to solve a real-world use case with machine learning using Amazon SageMaker
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. 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. A real life use case includes customer retention analysis to inform customer loyalty programs.
What you'll learn
- 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
- And much more
Who should take this course
- Developers
- Data scientists
What experience you'll need
- Familiarity with Python programming language
- Basic understanding of machine learning
Course overview
Level: Intermediate
Type: Classroom (virtual and in person)
Length: 1 day
Languages offered
This course is offered in the following languages: Bahasa Indonesia, English, French (France), German, Italian, Japanese, Korean, Portuguese (Brazil), Simplified Chinese, Spanish (Latin America), and Traditional Chinese.
We regularly update our courses based on customer feedback and AWS service updates. As a result, course content may vary between languages while we localize these updates.
Need more information?
Download the course outline for more information about what this course covers.
Looking for private training for your team?
With AWS-delivered private training, your team will learn actionable best practices together, tailored to your specific use cases.
Thinking about taking an exam?
Find a related exam to reinforce your learning.