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
2 hours difference
3 hours difference
4 hours difference
9 hours difference
German
2 hours difference
3 hours difference
Spanish
7 hours difference
9 hours difference
10 hours difference
Fast Lane will carry out all guaranteed training regardless of the number of attendees, exempt from force majeure or other unexpected events, like e.g. accidents or illness of the trainer, which prevent the course from being conducted.
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.
Middle East
Africa
Egypt
Europe
Germany
United Kingdom
This is a FLEX course, which is delivered both virtually and in the classroom.