Practical Data Science with Amazon SageMaker
Course Duration
1 Day
Audience
Employees of federal, state and local governments; and businesses working with the government.
Prerequisites
Familiarity with Python programming language Basic understanding of Machine Learning
Course Description
This course provides hands-on training in applying data science techniques using Amazon SageMaker. Students work through the full ML workflow including data wrangling, feature engineering, model training, evaluation, and deployment — gaining practical skills for solving real-world business problems with machine learning on AWS.
Learning 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 Outline
- 1 – Introduction to Machine Learning
- 2 – Introduction to Data Prep and SageMaker
- 3 – Problem formulation and Dataset Preparation
- 4 – Data Analysis and Visualization
- 5 – Training and Evaluating a Model
- 6 – Automatically Tune a Model
- 7 – Deployment / Production Readiness
- 8 – Relative Cost of Errors
- 9 – Amazon SageMaker architecture and features