AW-PDSASM
Practical Data Science with Amazon SageMaker (PDSASM)
Who should attend
- Developers
- Data Scientists
Prerequisites
- Familiarity with Python programming language
- Basic understanding of Machine Learning
Course Objectives
- 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
Course Content
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.
Schedule
English
1 hour difference
3 hours difference
8 hours difference
9 hours difference
German
2 hours difference
3 hours difference
Spanish
7 hours difference
8 hours difference
9 hours difference
10 hours difference
This computer icon in the schedule indicates that this date/time will be conducted as Instructor-Led Online Training.
This is a FLEX course, which is delivered both virtually and in the classroom.
This is a FLEX course, which is delivered both virtually and in the classroom.