Day One

Pre-assessment
Module 1: Introduction to Machine Learning and the ML Pipeline

Overview of machine learning, including use cases, types of machine learning, and key concepts
Overview of the ML pipeline
Introduction to course projects and approach
Module 2: Introduction to Amazon SageMaker

Introduction to Amazon SageMaker
Demo: Amazon SageMaker and Jupyter notebooks
Lab 1: Introduction to Amazon SageMaker
Module 3: Problem Formulation

Overview of problem formulation and deciding if ML is the right solution
Converting a business problem into an ML problem
Demo: Amazon SageMaker Ground Truth
Hands-on: Amazon SageMaker Ground Truth
Problem Formulation Exercise and Review
Project work for Problem Formulation

Day Two

Module 4: Preprocessing

Overview of data collection and integration, and techniques for data preprocessing and visualization
Lab 2: Data Preprocessing (including project work)
Module 5: Model Training

Choosing the right algorithm
Formatting and splitting your data for training
Loss functions and gradient descent for improving your model
Demo: Create a training job in Amazon SageMaker
Module 6: Model Training

How to evaluate classification models
How to evaluate regression models
Practice model training and evaluation
Train and evaluate project models
Lab 3: Model Training and Evaluation (including project work)
Project Share-Out 1
Module 7: Feature Engineering and Model Tuning

Feature extraction, selection, creation, and transformation
Hyperparameter tuning
Demo: SageMaker hyperparameter optimization

Day Three

Recap and Checkpoint #2

Module 6: Model Training

How to evaluate classification models
How to evaluate regression models
Practice model training and evaluation
Train and evaluate project models
Lab 3: Model Training and Evaluation (including project work)
Project Share-Out 1
Module 7: Feature Engineering and Model Tuning

Feature extraction, selection, creation, and transformation
Hyperparameter tuning
Demo: SageMaker hyperparameter optimization

Day Four

Lab 4: Feature Engineering (including project work)

Module 8: Module Deployment

How to deploy, inference, and monitor your model on Amazon SageMaker
Deploying ML at the edge
Module 9: Course Wrap-Up

Project Share-Out 2
Post-Assessment
Wrap-up