Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide

You're reading from   AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide The ultimate guide to passing the MLS-C01 exam on your first attempt

Arrow left icon
Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781835082201
Length 342 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Somanath Nanda Somanath Nanda
Author Profile Icon Somanath Nanda
Somanath Nanda
Weslley Moura Weslley Moura
Author Profile Icon Weslley Moura
Weslley Moura
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Machine Learning Fundamentals FREE CHAPTER 2. Chapter 2: AWS Services for Data Storage 3. Chapter 3: AWS Services for Data Migration and Processing 4. Chapter 4: Data Preparation and Transformation 5. Chapter 5: Data Understanding and Visualization 6. Chapter 6: Applying Machine Learning Algorithms 7. Chapter 7: Evaluating and Optimizing Models 8. Chapter 8: AWS Application Services for AI/ML 9. Chapter 9: Amazon SageMaker Modeling 10. Chapter 10: Model Deployment 11. Chapter 11: Accessing the Online Practice Resources 12. Other Books You May Enjoy

Image processing

Image processing is a very popular topic in machine learning. The idea is pretty self-explanatory: creating models that can analyze images and make inferences on top of them. By inference, you can understand this as detecting objects in an image, classifying images, and so on.

AWS offers a set of built-in algorithms you can use to train image processing models. In the next few sections, you will have a look at those algorithms.

Image classification algorithm

As the name suggests, the image classification algorithm is used to classify images using supervised learning. In other words, it needs a label within each image. It supports multi-label classification.

The way it operates is simple: during training, it receives an image and its associated labels. During inference, it receives an image and returns all the predicted labels. The image classification algorithm uses a CNN (ResNet) for training. It can either train the model from scratch or take advantage...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image