Overview
This Amazon Web Services (AWS) intermediate-level course – Practical Data Science with Amazon SageMaker focuses on equipping participants with the skills and knowledge to solve a real-world use-case with machine learning and produce actionable results using Amazon SageMaker. The course covers the different stages of a typical data science process for machine learning from analyzing and visualizing a dataset to preparing the data and feature engineering. In this course, participants will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. The course also includes a customer retention analysis for building effective 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
- Think critically about machine learning model results
Curriculum
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 the relative cost of errors
Who should attend
The course is intended for a technical audience at an intermediate level, such as –
- Developers
- Data scientists
Prerequisites
To attend this AWS course, participants need to be familiar with the Python programming language. They also need to have a basic understanding of machine learning.
