Practical Data Science with Amazon SageMaker (PDSASM)

Practical Data Science with Amazon SageMaker (PDSASM)

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.

- 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

- Developers
- Data Scientists

Module 1: Introduction to Machine Learning



- Types of ML
- Job Roles in ML
- Steps in the ML pipeline
Module 2: Introduction to Data Prep and SageMaker



- Training and Test dataset defined
- Introduction to SageMaker
- Demo: SageMaker console
- Demo: Launching a Jupyter notebook
Module 3: Problem formulation and Dataset Preparation



- Business Challenge: Customer churn
- Review Customer churn dataset
Module 4: Data Analysis and Visualization



- Demo: Loading and Visualizing your data...