No-Code Machine Learning Using Amazon AWS SageMaker Canvas

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Details

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Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

3 hours 35 minutes
Sessions

On-Demand
Level

Beginner
Subtitles

English

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Overview

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. As you progress, you’ll engage with four exciting machine learning projects, each designed to teach you how to build models from scratch, make predictions, and validate their accuracy. From detecting spam SMS messages to predicting customer churn and wine quality, these projects will help you grasp the real-world applications of machine learning. You’ll work with AWS services like S3 to store your data, and you'll become adept at creating models that require no coding knowledge. Each project reinforces the concepts covered, allowing you to practice and hone your skills. By the end of the course, you'll be well-equipped to tackle future machine learning challenges, armed with the skills to manage data, build powerful models, and perform predictions in a no-code environment. Additionally, you’ll explore versioning and dataset management to enhance your workflow. The course concludes with a hands-on assignment, giving you the opportunity to test your skills with a white wine quality prediction project, preparing you for independent ML work. This course is perfect for beginners in machine learning and data science who want to get started without writing code. It’s also ideal for business analysts, product managers, and professionals who wish to leverage machine learning to solve problems efficiently using AWS SageMaker Canvas. Basic familiarity with cloud platforms like AWS is recommended but not required.

Syllabus

Introduction to Machine Learning

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.

Introduction to AWS

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.

Introduction to SageMaker

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.

Setup

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.

SageMaker Canvas Interface Walkthrough

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.

Project 1 - Banknote Authentication

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.

Project 2 - Spam SMS Detection

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.

Project 3 - Customer Churn Prediction

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.

Project 4 - Wine Quality Prediction

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.

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.

Other Important Features in 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.

Congratulations and Next Steps

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.