Course Overview
You will 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. A real-life use case includes customer retention analysis to inform 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
Requirements
- Familiarity with Python programming language
- Basic understanding of Machine Learning
Target audiences
- Developers and Data Scientists
Curriculum
- 1 Section
- 9 Lessons
- 1 Day
Expand all sectionsCollapse all sections
- Topics9
- 1.1Module 1: Introduction to machine learning Types of ML Job Roles in ML Steps in the ML pipeline
- 1.2Module 2: Introduction to data prep and SageMaker Training and test dataset defined Introduction to SageMaker Demonstration: SageMaker console Demonstration: Launching a Jupyter notebook
- 1.3Module 3: Problem formulation and dataset preparation Business challenge: Customer churn Review customer churn dataset
- 1.4Module 4: Data analysis and visualization Demonstration: Loading and visualizing your dataset Exercise 1: Relating features to target variables Exercise 2: Relationships between attributes Demonstration: Cleaning the data
- 1.5Module 5: Training and evaluating a model Types of algorithms XGBoost and SageMaker Demonstration: Training the data Exercise 3: Finishing the estimator definition Exercise 4: Setting hyper parameters Exercise 5: Deploying the model Demonstration: hyper parameter tuning with SageMaker Demonstration: Evaluating model performance
- 1.6Module 6: Automatically tune a model Automatic hyper parameter tuning with SageMaker Exercises 6-9: Tuning jobs
- 1.7Module 7: Deployment / production readiness Deploying a model to an endpoint A/B deployment for testing Auto Scaling Demonstration: Configure and test auto scaling Demonstration: Check hyper parameter tuning job Demonstration: AWS Auto Scaling Exercise 10-11: Set up AWS Auto Scaling
- 1.8Module 8: Relative cost of errors Cost of various error types Demo: Binary classification cutoff
- 1.9Module 9: Amazon SageMaker architecture and features Accessing Amazon SageMaker notebooks in a VPC Amazon SageMaker batch transforms Amazon SageMaker Ground Truth Amazon SageMaker Neo
Instructor
