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Are you struggling with clean data labeling for your machine learning data sets? Amazon SageMaker Ground Truth helps you with automatic labeling and providing a managed experience for your end to end data labeling jobs.
Read more
Are you struggling with clean data labeling for your machine learning data sets? Amazon SageMaker Ground Truth helps you with automatic labeling and providing a managed experience for your end to end data labeling jobs.
Read more
Are you struggling with clean data labeling for your machine learning data sets? Amazon SageMaker Ground Truth helps you with automatic labeling and providing a managed experience for your end to end data labeling jobs.
Are you struggling with clean data labeling for your machine learning data sets? Amazon SageMaker Ground Truth helps you with automatic labeling and providing a managed experience for your end to end data labeling jobs. This lesson will explain the basics and provide a quick demo showing these capabilities; allow you to decide if Amazon SageMaker Ground Truth will work for your environment.
Register for this course and see more details by visiting:
OpenCourser.com/course/3rgrv5/introduction
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What's inside
Syllabus
Introduction to Amazon SageMaker Ground Truth
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Guides learners through the process of data labeling for machine learning data sets
Demonstrates how to use Amazon SageMaker Ground Truth for comprehensive data labeling
Appropriate for learners of varying levels of experience with data labeling
Instructors are recognized for expertise in the field of data labeling
Course materials focus on a niche topic, which may not be suitable for all
Could be more widely applicable if it covered a broader range of data labeling techniques
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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 Introduction to Amazon SageMaker Ground Truth with these
activities:
Review linear algebra
Show steps
Reviewing foundational linear algebra concepts covered in the course will help you grasp complex topics faster.
Browse courses on
Linear Algebra
Show steps
-
Revisit matrix operations, vector spaces, and linear transformations.
-
Solve practice problems involving eigenspaces.
-
Explain the concepts of orthogonality and least squares problems.
Review Artificial Intelligence for Dummies
Show steps
Provide a foundational overview of machine learning concepts and techniques to prepare for this course.
View
Artificial Intelligence For Dummies (For...
on Amazon
Show steps
-
Read chapters 1-4 to understand the basics of machine learning.
-
Complete the exercises at the end of each chapter to reinforce your understanding.
-
Summarize the key concepts covered in each chapter in your own words.
Complete Coursera's Introduction to AWS course
Show steps
Develop a strong understanding of AWS fundamentals to support your learning in this course.
Browse courses on
Cloud Computing
Show steps
-
Enroll in the Coursera course.
-
Watch the video lectures and complete the quizzes.
-
Set up an AWS account and experiment with the services covered in the course.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Explore Ground Truth Workflows
Show steps
Follow guided tutorials provided by AWS to understand the capabilities and workflows of the Ground Truth platform.
Browse courses on
Data Labeling
Show steps
-
Complete the Getting Started tutorial
-
Explore additional documentation and resources
Label and Annotate Data Using Ground Truth
Show steps
Practice working with the Ground Truth platform to gain proficiency in data labeling techniques for machine learning datasets.
Browse courses on
Data Labeling
Show steps
-
Create a Ground Truth labeling job
-
Upload data for labeling
-
Define labeling tasks
-
Review and approve data labels
Complete 10 AWS Code Challenges
Show steps
Sharpen your AWS skills by solving real-world problems and gain confidence in your abilities.
Show steps
-
Identify 10 AWS Code Challenges that align with the topics covered in this course.
-
Solve the challenges using the AWS CLI or SDK.
-
Review your solutions and identify areas for improvement.
Practice solving machine learning classification problems
Show steps
Solving practical classification problems will reinforce what you learn in the course.
Show steps
-
Implement a logistic regression model to classify data.
-
Evaluate the performance of your model using different evaluation metrics.
-
Fine-tune the hyperparameters of your model to improve accuracy.
Join a Study Group for this Course
Show steps
Connect with classmates, engage in discussions, and collaborate on assignments to enhance your learning experience.
Show steps
-
Identify potential study partners.
-
Establish a regular meeting schedule.
-
Discuss course material, share insights, and work through problems together.
Explore additional resources on Amazon SageMaker Ground Truth
Show steps
Exploring additional resources will deepen your understanding of the course material.
Show steps
-
Review the Amazon SageMaker Ground Truth documentation.
-
Watch video tutorials on how to use Amazon SageMaker Ground Truth.
-
Join online forums and discussion groups related to Amazon SageMaker Ground Truth.
Build a Simple Machine Learning Model
Show steps
Apply your knowledge to a hands-on project, solidifying your understanding of machine learning principles.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a dataset relevant to your interests.
-
Train a machine learning model using the dataset.
-
Evaluate the performance of your model using metrics such as accuracy and precision.
Build a machine learning project using Amazon SageMaker
Show steps
Building an end-to-end machine learning project will give you valuable hands-on experience.
Show steps
-
Define the problem you want to solve and gather relevant data.
-
Train and evaluate a machine learning model on Amazon SageMaker.
-
Deploy the trained model and monitor its performance.
Build a Data Labeling Pipeline Using Ground Truth
Show steps
Create a complete data labeling pipeline that leverages the capabilities of Ground Truth to automate and improve the quality of labeled data.
Browse courses on
Data Labeling
Show steps
-
Design the labeling pipeline
-
Integrate Ground Truth into the pipeline
-
Validate and evaluate the pipeline
Mentor a Junior Student
Show steps
Deepen your understanding of the course material by explaining concepts to others and providing guidance.
Show steps
-
Identify a junior student who is struggling with the course material.
-
Set up regular mentoring sessions.
-
Review course concepts, answer questions, and provide support.
Create a Machine Learning Model with Ground Truth Annotated Data
Show steps
Develop a machine learning model that utilizes the labeled data generated by Ground Truth to improve the accuracy and efficiency of the model.
Browse courses on
Data Labeling
Show steps
-
Define the machine learning task
-
Train the model using Ground Truth annotated data
-
Evaluate and deploy the model
Contribute to the SageMaker Ground Truth Open Source Project
Show steps
Gain practical experience with SageMaker Ground Truth by contributing to its open source project, expanding your knowledge and potentially helping to improve the library.
Browse courses on
Python Programming
Show steps
-
Identify an issue or feature to contribute to.
-
Fork the repository and create a branch for your changes.
-
Make your changes and submit a pull request.
Review linear algebra
Show steps
Reviewing foundational linear algebra concepts covered in the course will help you grasp complex topics faster.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revisit matrix operations, vector spaces, and linear transformations.
- Solve practice problems involving eigenspaces.
- Explain the concepts of orthogonality and least squares problems.
Review Artificial Intelligence for Dummies
Show steps
Provide a foundational overview of machine learning concepts and techniques to prepare for this course.
View
Artificial Intelligence For Dummies (For...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-4 to understand the basics of machine learning.
- Complete the exercises at the end of each chapter to reinforce your understanding.
- Summarize the key concepts covered in each chapter in your own words.
Complete Coursera's Introduction to AWS course
Show steps
Develop a strong understanding of AWS fundamentals to support your learning in this course.
Browse courses on
Cloud Computing
Show steps
Show steps
Show steps
- Enroll in the Coursera course.
- Watch the video lectures and complete the quizzes.
- Set up an AWS account and experiment with the services covered in the course.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Explore Ground Truth Workflows
Show steps
Follow guided tutorials provided by AWS to understand the capabilities and workflows of the Ground Truth platform.
Browse courses on
Data Labeling
Show steps
Show steps
Show steps
- Complete the Getting Started tutorial
- Explore additional documentation and resources
Label and Annotate Data Using Ground Truth
Show steps
Practice working with the Ground Truth platform to gain proficiency in data labeling techniques for machine learning datasets.
Browse courses on
Data Labeling
Show steps
Show steps
Show steps
- Create a Ground Truth labeling job
- Upload data for labeling
- Define labeling tasks
- Review and approve data labels
Complete 10 AWS Code Challenges
Show steps
Sharpen your AWS skills by solving real-world problems and gain confidence in your abilities.
Show steps
Show steps
Show steps
- Identify 10 AWS Code Challenges that align with the topics covered in this course.
- Solve the challenges using the AWS CLI or SDK.
- Review your solutions and identify areas for improvement.
Practice solving machine learning classification problems
Show steps
Solving practical classification problems will reinforce what you learn in the course.
Show steps
Show steps
Show steps
- Implement a logistic regression model to classify data.
- Evaluate the performance of your model using different evaluation metrics.
- Fine-tune the hyperparameters of your model to improve accuracy.
Join a Study Group for this Course
Show steps
Connect with classmates, engage in discussions, and collaborate on assignments to enhance your learning experience.
Show steps
Show steps
Show steps
- Identify potential study partners.
- Establish a regular meeting schedule.
- Discuss course material, share insights, and work through problems together.
Explore additional resources on Amazon SageMaker Ground Truth
Show steps
Exploring additional resources will deepen your understanding of the course material.
Show steps
Show steps
Show steps
- Review the Amazon SageMaker Ground Truth documentation.
- Watch video tutorials on how to use Amazon SageMaker Ground Truth.
- Join online forums and discussion groups related to Amazon SageMaker Ground Truth.
Build a Simple Machine Learning Model
Show steps
Apply your knowledge to a hands-on project, solidifying your understanding of machine learning principles.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a dataset relevant to your interests.
- Train a machine learning model using the dataset.
- Evaluate the performance of your model using metrics such as accuracy and precision.
Build a machine learning project using Amazon SageMaker
Show steps
Building an end-to-end machine learning project will give you valuable hands-on experience.
Show steps
Show steps
Show steps
- Define the problem you want to solve and gather relevant data.
- Train and evaluate a machine learning model on Amazon SageMaker.
- Deploy the trained model and monitor its performance.
Build a Data Labeling Pipeline Using Ground Truth
Show steps
Create a complete data labeling pipeline that leverages the capabilities of Ground Truth to automate and improve the quality of labeled data.
Browse courses on
Data Labeling
Show steps
Show steps
Show steps
- Design the labeling pipeline
- Integrate Ground Truth into the pipeline
- Validate and evaluate the pipeline
Mentor a Junior Student
Show steps
Deepen your understanding of the course material by explaining concepts to others and providing guidance.
Show steps
Show steps
Show steps
- Identify a junior student who is struggling with the course material.
- Set up regular mentoring sessions.
- Review course concepts, answer questions, and provide support.
Create a Machine Learning Model with Ground Truth Annotated Data
Show steps
Develop a machine learning model that utilizes the labeled data generated by Ground Truth to improve the accuracy and efficiency of the model.
Browse courses on
Data Labeling
Show steps
Show steps
Show steps
- Define the machine learning task
- Train the model using Ground Truth annotated data
- Evaluate and deploy the model
Contribute to the SageMaker Ground Truth Open Source Project
Show steps
Gain practical experience with SageMaker Ground Truth by contributing to its open source project, expanding your knowledge and potentially helping to improve the library.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Identify an issue or feature to contribute to.
- Fork the repository and create a branch for your changes.
- Make your changes and submit a pull request.
Career center
Learners who complete Introduction to Amazon SageMaker Ground Truth will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets, will find Amazon SageMaker Ground Truth useful. This platform can help build a foundation for a Data Analyst who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Data Scientist
Data Scientist
Data Scientists who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets, will find Amazon SageMaker Ground Truth useful. This platform can help build a foundation for a Data Scientist who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for the development and deployment of Machine Learning models. Amazon SageMaker Ground Truth can help build a foundation for a Machine Learning Engineer, as many of their duties include organizing, training, and evaluating human labelers to collect large samples of data to train a Machine Learning model.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure and pipelines that move data around an organization. Amazon SageMaker Ground Truth can help build a foundation for a Data Engineer, as it can be used to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts collect, analyze, and interpret data to help businesses improve their performance and make data-driven decisions. Amazon SageMaker Ground Truth can help build a foundation for a Business Intelligence Analyst, as it can be used to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Software Engineer
Software Engineer
This course may be useful for Software Engineers who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Software Engineer who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Product Manager
Product Manager
This course may be useful for Product Managers who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Product Manager who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Cloud Architect
Cloud Architect
This course may be useful for Cloud Architects who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Cloud Architect who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Data Architect
Data Architect
This course may be useful for Data Architects who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Data Architect who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Systems Analyst
Systems Analyst
This course may be useful for Systems Analysts who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Systems Analyst who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Business Analyst
Business Analyst
This course may be useful for Business Analysts who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Business Analyst who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Database Administrator
Database Administrator
This course may be useful for Database Administrators who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Database Administrator who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
IT Manager
IT Manager
This course may be useful for IT Managers who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for an IT Manager who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Data Governance Specialist
Data Governance Specialist
This course may be useful for Data Governance Specialists who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Data Governance Specialist who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Project Manager
Project Manager
This course may be useful for Project Managers who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Project Manager who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
For more career information including salaries, visit:
OpenCourser.com/course/3rgrv5/introduction
Reading list
We've selected 11 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
Introduction to Amazon SageMaker Ground Truth.
Save
Comprehensive guide to deep learning, covering the latest advances in the field. It provides a deep understanding of the fundamental concepts of deep learning, as well as practical tips on how to build and train deep learning models.
Provides a comprehensive overview of deep learning for natural language processing, covering topics such as text classification, machine translation, and question answering. It great resource for anyone looking to learn more about this field.
Save
Provides a comprehensive overview of computer vision, covering topics such as image processing, feature extraction, and object recognition. It great resource for anyone looking to learn more about this field.
Provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language understanding, and machine translation. It great resource for anyone looking to learn more about this field.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for anyone looking to learn more about this field.
Provides a comprehensive overview of machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for anyone looking to learn more about machine learning using Python.
Provides a comprehensive overview of machine learning using R. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for anyone looking to learn more about machine learning using R.
Hands-on guide to data science, covering the entire data science pipeline from data collection to model deployment. It great resource for anyone looking to learn the basics of data science.
Practical guide to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It provides a comprehensive overview of machine learning techniques and algorithms.
Provides a gentle introduction to machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for anyone looking to get started with machine learning.
Provides a very gentle introduction to machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for anyone who is completely new to machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3rgrv5/introduction
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Good to know
Guides learners through the process of data labeling for machine learning data sets
Demonstrates how to use Amazon SageMaker Ground Truth for comprehensive data labeling
Appropriate for learners of varying levels of experience with data labeling
Instructors are recognized for expertise in the field of data labeling
Course materials focus on a niche topic, which may not be suitable for all
Could be more widely applicable if it covered a broader range of data labeling techniques
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