Temario

Day One

Module 0: Introduction

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
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
Day Two

Checkpoint 1 and Answer Review

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
Day Three

Checkpoint 2 and Answer Review

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
Day Four

Checkpoint 3 and Answer Review

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