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
• Hands-on: Amazon SageMaker and Jupyter notebooks

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
• Practice problem formulation
• Formulate problems for projects

Module 4: Preprocessing

• Overview of data collection and integration, and techniques for data preprocessing and
• visualization
• Practice preprocessing
• Preprocess project data
• Class discussion about projects

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 Evaluation
• How to evaluate classification models
• How to evaluate regression models
• Practice model training and evaluation
• Train and evaluate project models
• Initial project presentations

Module 7: Feature Engineering and Model Tuning

• Feature extraction, selection, creation, and transformation
• Hyperparameter tuning
• Demo: SageMaker hyperparameter optimization
• Practice feature engineering and model tuning
• Apply feature engineering and model tuning to projects
• Final project presentations

Module 8: Deployment

• How to deploy, inference, and monitor your model on Amazon SageMaker
• Deploying ML at the edge
• Demo: Creating an Amazon SageMaker endpoint
• Post-assessment
• Course wrap-up