Description

Introduction

No-Code ML with AWS SageMaker Canvas is designed for business analysts, domain experts, and non-technical users who want to build machine learning models without writing any code. SageMaker Canvas is a visual, point-and-click interface that simplifies the machine learning workflow, making it accessible to anyone with basic data knowledge. This course guides you through creating accurate predictions, exploring datasets, and sharing results—all without needing to know programming or data science in depth.

Prerequisites

To make the most of this course, learners should have:

• A basic understanding of business data and its use in decision-making.

• Experience working with spreadsheets or BI tools (e.g., Excel, Tableau).

• An AWS account with SageMaker Canvas access enabled.

• No coding or machine learning experience is required.

Table of Contents

1. Introduction to No-Code Machine Learning

   • 1.1 What is No-Code ML?

   • 1.2 Benefits of No-Code ML for Business Users

   • 1.3 Use Cases in Marketing, Finance, Healthcare, and More

2. Overview of AWS SageMaker Canvas

   • 2.1 What is SageMaker Canvas?

   • 2.2 Key Features and Capabilities

   • 2.3 How It Integrates with Other AWS Services

3. Setting Up SageMaker Canvas

   • 3.1 Accessing SageMaker Canvas from the AWS Console

   • 3.2 Navigating the Canvas Interface

   • 3.3 Connecting to Data Sources (S3, Redshift, Snowflake)

4. Preparing and Importing Data

   • 4.1 Uploading and Importing CSV Files

   • 4.2 Exploring and Visualizing Data

   • 4.3 Cleaning and Transforming Data (Point-and-Click)

5. Building Machine Learning Models

   • 5.1 Choosing the Right Problem Type (Prediction or Classification)

   • 5.2 Running AutoML with SageMaker Canvas

   • 5.3 Understanding Model Performance and Metrics

6. Generating Predictions and Insights

   • 6.1 Using Trained Models for Batch Predictions

   • 6.2 Visualizing Prediction Results

   • 6.3 Exporting Results to Excel or Sharing with SageMaker Studio

7. Best Practices and Responsible AI

   • 7.1 Interpreting ML Results Accurately

   • 7.2 Ensuring Data Privacy and Compliance

   • 7.3 Model Monitoring and Reusability

AWS SageMaker Canvas empowers business users to unlock the power of machine learning without writing code. By the end of this course, you’ll be able to prepare data, build accurate models, and generate predictions with ease. This no-code approach enables smarter decision-making across teams and lowers the barrier to ML adoption in your organization.