In this module, we will explore Amazon Web Services (AWS), the platform that powers SageMaker Canvas. You’ll learn what AWS is, its key services, and how to sign in to the AWS console for cloud-based machine learning activities.

In this module, we will dive into Amazon SageMaker, a powerful tool for building and training machine learning models. You’ll also get introduced to SageMaker Canvas, the no-code interface that enables you to create models without needing programming skills.

In this module, we will walk through setting up your SageMaker domain and user environment. Additionally, you'll learn how to configure data in S3 Buckets, ensuring everything is ready for building machine learning models in SageMaker.

In this module, we will explore the SageMaker Canvas interface, guiding you through its various features and functionalities. This walkthrough will help you efficiently navigate and use SageMaker Canvas for machine learning tasks.

In this module, we will apply what we've learned to build a model for banknote authentication. You'll gather training data, build a predictive model, and validate its performance through batch prediction and accuracy testing.

In this module, we will focus on detecting spam SMS messages using machine learning. You’ll learn how to prepare your data, build a model, and evaluate its predictions to ensure it accurately detects spam.

In this module, we will predict customer churn using machine learning. You'll import relevant customer data, build a predictive model, and assess its ability to forecast churn rates accurately.

In this module, we will create a model to predict wine quality. You will work with datasets, build a model, and test its performance, learning how to combine multiple data sources for better results.

In this module, you will complete an assignment where you predict white wine quality. This hands-on exercise will reinforce your learning and improve your ability to apply machine learning techniques using SageMaker Canvas.

In this module, we will cover the versioning feature in SageMaker Canvas. You'll learn how to manage different versions of your models, ensuring you can track changes and improvements over time.

In this module, we will conclude the course with tips on obtaining more datasets, getting help with SageMaker Canvas, and congratulating you on completing the course. You'll also receive guidance on your next steps in mastering no-code machine learning.