In this course, you'll explore the vast potential of machine learning with Amazon AWS SageMaker Canvas, a no-code platform. You'll begin with an introduction to the fundamentals of machine learning, AWS, and the core features of SageMaker. By walking through the SageMaker Canvas interface, you'll learn how to set up a SageMaker domain, manage users, and prepare your data for machine learning projects. This essential groundwork ensures you’re ready to dive into the hands-on elements of the course.
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Recommended experience
What you'll learn
Understand machine learning basics and AWS SageMaker Canvas, including data setup and environment prep.
Apply ML techniques to build, train, and test models on real-world datasets in a no-code platform.
Analyze and interpret model predictions to validate accuracy and improve performance.
Create no-code machine learning solutions to solve business problems using AWS SageMaker Canvas.
Details to know
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October 2024
5 assignments
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There are 12 modules in this course
In this module, we will introduce the basics of machine learning, covering fundamental concepts and applications. You will gain an understanding of what machine learning is and how it works, setting the foundation for the rest of the course.
What's included
2 videos1 reading
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.
What's included
2 videos
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.
What's included
2 videos
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.
What's included
2 videos1 assignment
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.
What's included
1 video
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.
What's included
4 videos
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.
What's included
3 videos1 assignment
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.
What's included
3 videos
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.
What's included
3 videos1 assignment
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.
What's included
1 video
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.
What's included
1 video1 assignment
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.
What's included
3 videos1 assignment
Instructor
Offered by
Recommended if you're interested in Machine Learning
Duke University
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.