We may earn an affiliate commission when you visit our partners.
Course image
Amit Yadav

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please contact AWS support and request access.

Read more

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please contact AWS support and request access.

In this 2-hour long project-based course, you will learn how to train and deploy a Semantic Segmentation model using Amazon Sagemaker. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. We will use the semantic segmentation algorithm from Sagemaker to create, train and deploy a model that will be able to segment images of dogs and cats from the popular IIIT-Oxford Pets Dataset into 3 unique pixel values. That is, each pixel of an input image would be classified as either foreground (pet), background (not a pet), or unclassified (transition between foreground and background).

Since this is a practical, project-based course, we will not dive in the theory behind deep learning based semantic segmentation, but will focus purely on training and deploying a model with Sagemaker. You will also need to have some experience with Amazon Web Services (AWS).

Enroll now

What's inside

Syllabus

Semantic Segmentation with Amazon Sagemaker
In this 2-hour long project-based course, you will learn how to train and deploy a Semantic Segmentation model using Amazon Sagemaker. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. We will use the semantic segmentation algorithm from Sagemaker to create, train and deploy a model that will be able to segment images of dogs and cats from the popular IIIT-Oxford Pets Dataset into 3 unique pixel values. That is, each pixel of an input image would be classified as either foreground (pet), background (not a pet), or unclassified (transition between foreground and background).

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Examines semantic segmentation, which is indispensable to image and object recognition
Leverages the robust capabilities of Amazon Sagemaker
Taught by Amit Yadav, who is recognized for their expertise in computer vision
Develops practical, applicable skills in semantic segmentation
Tailored for learners with experience in Python programming and AWS

Save this course

Save Semantic Segmentation with Amazon Sagemaker to your list so you can find it easily later:
Save

Reviews summary

Sagemaker semantic segmentation

According to students, Semantic Segmentation with Amazon Sagemaker is a popular course, especially for those new to using Sagemaker. Students praise the course for its well-organized content, step-by-step instructions, and practical projects. While the course may be challenging for those without prior programming experience, students who complete it feel prepared to use Sagemaker for their own projects.
Good introduction to Sagemaker for those new to the platform.
"I found the project to be a great step-by-step introduction to using notebooks within sagemaker in order to orchestrate training/deployment jobs!"
Projects provide hands-on experience with Sagemaker.
"better experience"
May be challenging for those without programming experience.
"I am not very happy with this course."
"The instructor just rushes through some inside his formerly prepared jupyter notebook and his explanations on the actual code snippets are very short and not very understandable."
"Information given is not complete"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Semantic Segmentation with Amazon Sagemaker with these activities:
Read the book 'Deep Learning for Computer Vision' by Jason Brownlee
This will provide you with a comprehensive understanding of deep learning for computer vision, including semantic segmentation.
Show steps
  • Obtain a copy of the book.
  • Read the book thoroughly, taking notes and highlighting important sections.
  • Complete the exercises and activities in the book.
Find a mentor who can provide guidance on semantic segmentation
This will help you connect with experts in the field and gain valuable insights.
Browse courses on Amazon SageMaker
Show steps
  • Identify potential mentors through online forums, professional networking sites, or research institutions.
  • Reach out to potential mentors and express your interest in their mentorship.
  • Schedule a meeting to discuss your goals and expectations.
Review Python programming fundamentals
This will help you review the basics of Python, including data types, control flow, and functions.
Show steps
  • Go through an online Python tutorial or course.
  • Review your notes from previous Python courses or projects.
  • Solve some basic Python coding challenges.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow a tutorial on using Amazon Sagemaker for semantic segmentation
This will help you gain hands-on experience with using Sagemaker for semantic segmentation.
Browse courses on Amazon SageMaker
Show steps
  • Find a tutorial on the official Amazon Sagemaker website or other reputable sources.
  • Follow the tutorial step-by-step, completing all the exercises and activities.
  • Make sure to understand the concepts and techniques used in the tutorial.
Solve semantic segmentation problems on LeetCode or Kaggle
This will help you develop your problem-solving and coding skills in semantic segmentation.
Browse courses on Amazon SageMaker
Show steps
  • Create an account on LeetCode or Kaggle.
  • Find semantic segmentation problems to solve.
  • Implement your solutions using Python and the Sagemaker Python SDK.
  • Submit your solutions and review your performance.
Mentor other students in this course
This will help you reinforce your understanding of the course material and develop your communication and mentoring skills.
Show steps
  • Join a study group or online forum for the course.
  • Offer to help other students with their understanding of the material.
  • Answer questions and provide guidance to other students.
  • Share your knowledge and experience with others.
Create a sample semantic segmentation project using Amazon Sagemaker
This will allow you to apply the skills you learned in the course to a practical project.
Browse courses on Amazon SageMaker
Show steps
  • Choose a dataset for semantic segmentation, such as the IIIT-Oxford Pets Dataset.
  • Create a Sagemaker notebook instance.
  • Use the Sagemaker Python SDK to train and deploy a semantic segmentation model.
  • Evaluate the performance of your model.
  • Write a report summarizing your project and findings.
Participate in a semantic segmentation competition
This will challenge you to apply your skills in a competitive environment and learn from others.
Browse courses on Amazon SageMaker
Show steps
  • Find a semantic segmentation competition that is relevant to your interests.
  • Gather a team of participants.
  • Develop a strategy for solving the competition problem.
  • Implement your solution using Amazon Sagemaker and other relevant tools.
  • Submit your solution to the competition.

Career center

Learners who complete Semantic Segmentation with Amazon Sagemaker will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers design, implement, and evaluate machine learning models to solve real-world problems. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, analyze, and interpret data to identify trends and patterns. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Computer Vision Engineer
Computer Vision Engineers develop and implement algorithms that allow computers to see and understand images and videos. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Product Manager
Product Managers are responsible for the development and success of a product. They work with engineers, designers, and marketers to bring a product to market. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Business Analyst
Business Analysts help businesses to identify and solve problems. They use data and analysis to identify opportunities and risks. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with stakeholders to ensure that projects are completed on time, within budget, and to the desired specifications. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
UX Designer
UX Designers design and evaluate user experiences. They work with engineers and product managers to create products that are easy to use and enjoyable to interact with. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Marketing Manager
Marketing Managers are responsible for planning and executing marketing campaigns. They work with marketing teams to create and implement marketing strategies. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Sales Manager
Sales Managers are responsible for leading and managing sales teams. They work with sales teams to develop and implement sales strategies. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products and services. They work with customers to resolve issues and provide support. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Technical Writer
Technical Writers create and edit technical documentation. They work with engineers and product managers to create documentation that is easy to understand and use. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
Quality Assurance Analyst
Quality Assurance Analysts test and evaluate software products. They work with developers to identify and fix bugs. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.
IT Support Specialist
IT Support Specialists provide technical support to users. They help users to troubleshoot and resolve technical issues. This course provides a foundation in semantic segmentation, a type of machine learning model that can be used to identify and segment objects in images. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own semantic segmentation models, which can be used in a variety of applications, such as image segmentation, object detection, and medical imaging.

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Semantic Segmentation with Amazon Sagemaker.
Provides a comprehensive overview of deep learning for computer vision, including the fundamentals of convolutional neural networks and popular architectures such as ResNet and Inception. It covers advanced techniques such as object detection, semantic segmentation, and instance segmentation.
Provides a comprehensive overview of deep learning, covering topics such as neural networks, training algorithms, and applications. It good resource for understanding the theoretical foundations of deep learning.
Provides a comprehensive overview of computer vision, covering topics such as image formation, feature extraction, object recognition, and image segmentation. It good resource for understanding the theoretical foundations of semantic segmentation.
Provides a comprehensive overview of computer vision, covering topics such as image formation, feature extraction, object recognition, and image segmentation. It good resource for understanding the theoretical foundations of semantic segmentation.
Provides a comprehensive overview of computer vision, covering topics such as image formation, feature extraction, object recognition, and image segmentation. It good resource for understanding the theoretical foundations of semantic segmentation.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Semantic Segmentation with Amazon Sagemaker.
Using TensorFlow with Amazon Sagemaker
Most relevant
Object Detection with Amazon Sagemaker
Most relevant
Image Classification with Amazon Sagemaker
Most relevant
Building Recommendation System Using MXNET on AWS...
Most relevant
AWS Computer Vision: Getting Started with GluonCV
Most relevant
Amazon SageMaker
Generative AI Foundations for Cloud
AWS Lambda and API Gateway Basics - Build Serverless...
Self-Driving Car Engineer - Advanced Deep Learning
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser