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Anish Mohan,
Abhilash Somasamudramath,
Antje Barth,
Adam Tetelman,
Pavan Kumar Sunder,
Isaac Privitera,
and
Chris Fregly
Machine learning (ML) projects can be complex, tedious, and time consuming. AWS and NVIDIA solve this challenge with fast, effective, and easy-to-use capabilities for your ML project.
This course is designed for ML practitioners, including data scientists...
Read more
Machine learning (ML) projects can be complex, tedious, and time consuming. AWS and NVIDIA solve this challenge with fast, effective, and easy-to-use capabilities for your ML project.
This course is designed for ML practitioners, including data scientists...
Read more
Machine learning (ML) projects can be complex, tedious, and time consuming. AWS and NVIDIA solve this challenge with fast, effective, and easy-to-use capabilities for your ML project.
This course is designed for ML practitioners, including data scientists and developers, who have a working knowledge of machine learning workflows. In this course, you will gain hands-on experience on building, training, and deploying scalable machine learning models with Amazon SageMaker and Amazon EC2 instances powered by NVIDIA GPUs. Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. Amazon EC2 instances powered by NVIDIA GPUs along with NVIDIA software offer high performance GPU-optimized instances in the cloud for efficient model training and cost effective model inference hosting.
In this course, you will first get an overview of Amazon SageMaker and NVIDIA GPUs. Then, you will get hands-on, by running a GPU powered Amazon SageMaker notebook instance. You will then learn how to prepare a dataset for model training, build a model, execute model training, and deploy and optimize the ML model. You will also learn, hands-on, how to apply this workflow for computer vision (CV) and natural language processing (NLP) use cases. After completing this course, you will be able to build, train, deploy, and optimize ML workflows with GPU acceleration in Amazon SageMaker and understand the key Amazon SageMaker services applicable to computer vision and NLP ML tasks.
Register for this course and see more details by visiting:
OpenCourser.com/course/9zfcnr/hands
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Teaches learners how to build, train, deploy, and optimize ML workflows with GPU acceleration in Amazon SageMaker
Provides hands-on experience in computer vision and natural language processing use cases
Teaches learners how to prepare a dataset for model training
Emphasizes the use of NVIDIA GPUs for efficient model training and cost-effective model inference hosting
Taught by instructors from AWS, NVIDIA, and industry professionals
Requires prior knowledge of machine learning workflows
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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 Hands-on Machine Learning with AWS and NVIDIA with these
activities:
Attend a workshop on Python for machine learning
Show steps
Python is the primary programming language used by data scientists. A workshop can help those not very familiar with Python to get up to speed and learn the skills needed to be successful.
Browse courses on
Python
Show steps
-
Find a workshop
-
Attend the workshop
-
Participate in the exercises and discussions
Follow tutorial on Amazon SageMaker
Show steps
This course is best suited for those who have a basic understanding of machine learning. Following a tutorial on AWS and NVIDIA can provide that foundation to build from.
Browse courses on
Amazon SageMaker
Show steps
-
Search for beginners tutorials on Amazon SageMaker
-
Find tutorials thatcover getting started with basic machine learning concepts
-
Apply the tutorial by following it and completing all steps
Review linear algebra
Show steps
Linear algebra is used heavily throughout this course. A strong understanding of these concepts before starting the course will set learners up for success
Browse courses on
Linear Algebra
Show steps
-
Review notes from a previous course
-
Find online resources for reviewing linear algebra
-
Do practice problems to test for understanding
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review the book 'Deep Learning with Python'
Show steps
This book provides an excellent overview of deep learning, from its foundations to practical implementation. Reviewing this book will provide a strong understanding of these techniques in machine learning.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read the book
-
Take notes and highlight key concepts
-
Test your understanding by completing the exercises and projects in the book
Join a study group for the course
Show steps
Talking about course concepts with peers has been shown to improve retention. A study group can be an excellent way to go over the material covered in class.
Show steps
-
Find a study group
-
Participate in group discussions
-
Review and discuss the material covered in class
Practice deploying a model on Amazon SageMaker
Show steps
There are many models that can be deployed and there is no single way that works in every case. This activity is designed to have you repeat and refine the deployment process to build confidence.
Browse courses on
Model Deployment
Show steps
-
Choose a pre-built model or create your own
-
Follow documentation to deploy the model on Amazon SageMaker
-
Test and verify the deployment
-
Repeat the process several times
Develop a Computer Vision project using Amazon SageMaker
Show steps
Many of the class projects are based on machine learning models. This activity provides an opportunity to apply those models to work on a more complex project simulating real world use cases.
Browse courses on
Deep Learning
Show steps
-
Come up with a project idea
-
Develop and extend the Amazon SageMaker notebook instances
-
Gther the data and prepare it
-
Build, train, and deploy your model
-
Present your project
Create a presentation on a topic of your choice related to the course
Show steps
Creating a presentation forces you to summarize and distill complex concepts into smaller more digestible chunks. This activity is designed to help reinforce the knowledge you gain through the course.
Browse courses on
Machine Learning
Show steps
-
Choose a presentation topic
-
Research the topic and create a slide deck
-
Practice presenting it
-
Present the presentation
Attend a workshop on Python for machine learning
Show steps
Python is the primary programming language used by data scientists. A workshop can help those not very familiar with Python to get up to speed and learn the skills needed to be successful.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find a workshop
- Attend the workshop
- Participate in the exercises and discussions
Follow tutorial on Amazon SageMaker
Show steps
This course is best suited for those who have a basic understanding of machine learning. Following a tutorial on AWS and NVIDIA can provide that foundation to build from.
Browse courses on
Amazon SageMaker
Show steps
Show steps
Show steps
- Search for beginners tutorials on Amazon SageMaker
- Find tutorials thatcover getting started with basic machine learning concepts
- Apply the tutorial by following it and completing all steps
Review linear algebra
Show steps
Linear algebra is used heavily throughout this course. A strong understanding of these concepts before starting the course will set learners up for success
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review notes from a previous course
- Find online resources for reviewing linear algebra
- Do practice problems to test for understanding
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review the book 'Deep Learning with Python'
Show steps
This book provides an excellent overview of deep learning, from its foundations to practical implementation. Reviewing this book will provide a strong understanding of these techniques in machine learning.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read the book
- Take notes and highlight key concepts
- Test your understanding by completing the exercises and projects in the book
Join a study group for the course
Show steps
Talking about course concepts with peers has been shown to improve retention. A study group can be an excellent way to go over the material covered in class.
Show steps
Show steps
Show steps
- Find a study group
- Participate in group discussions
- Review and discuss the material covered in class
Practice deploying a model on Amazon SageMaker
Show steps
There are many models that can be deployed and there is no single way that works in every case. This activity is designed to have you repeat and refine the deployment process to build confidence.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Choose a pre-built model or create your own
- Follow documentation to deploy the model on Amazon SageMaker
- Test and verify the deployment
- Repeat the process several times
Develop a Computer Vision project using Amazon SageMaker
Show steps
Many of the class projects are based on machine learning models. This activity provides an opportunity to apply those models to work on a more complex project simulating real world use cases.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Come up with a project idea
- Develop and extend the Amazon SageMaker notebook instances
- Gther the data and prepare it
- Build, train, and deploy your model
- Present your project
Create a presentation on a topic of your choice related to the course
Show steps
Creating a presentation forces you to summarize and distill complex concepts into smaller more digestible chunks. This activity is designed to help reinforce the knowledge you gain through the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a presentation topic
- Research the topic and create a slide deck
- Practice presenting it
- Present the presentation
Career center
Learners who complete Hands-on Machine Learning with AWS and NVIDIA will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course can help data scientists build a strong foundation in machine learning, which is a key skill for success in this role.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers are responsible for designing, building, and deploying machine learning models. This course can help machine learning engineers gain the skills and knowledge they need to be successful in this role.
Software Engineer
Software Engineer
Software engineers are responsible for designing, developing, and maintaining software applications. This course can help software engineers build a strong foundation in machine learning, which is a key skill for success in this role.
Data Analyst
Data Analyst
Data analysts are responsible for collecting, cleaning, and analyzing data. This course can help data analysts build a strong foundation in machine learning, which is a key skill for success in this role.
Business Analyst
Business Analyst
Business analysts are responsible for helping businesses understand their data and make informed decisions. This course can help business analysts build a strong foundation in machine learning, which is a key skill for success in this role.
Product Manager
Product Manager
Product managers are responsible for managing the development and launch of new products. This course can help product managers understand the role of machine learning in product development and how to use machine learning to improve products.
Marketing Manager
Marketing Manager
Marketing managers are responsible for developing and executing marketing campaigns. This course can help marketing managers understand the role of machine learning in marketing and how to use machine learning to improve marketing campaigns.
Sales Manager
Sales Manager
Sales managers are responsible for managing sales teams and achieving sales goals. This course can help sales managers understand the role of machine learning in sales and how to use machine learning to improve sales performance.
Compliance Officer
Compliance Officer
Compliance officers are responsible for ensuring that a business complies with all applicable laws and regulations. This course can help compliance officers understand the role of machine learning in compliance and how to use machine learning to improve compliance practices.
Financial Analyst
Financial Analyst
Financial analysts are responsible for analyzing financial data and making recommendations to investors. This course can help financial analysts understand the role of machine learning in financial analysis and how to use machine learning to improve investment decisions.
Operations Manager
Operations Manager
Operations managers are responsible for overseeing the day-to-day operations of a business. This course can help operations managers understand the role of machine learning in operations and how to use machine learning to improve operational efficiency.
Risk Manager
Risk Manager
Risk managers are responsible for identifying and mitigating risks to a business. This course can help risk managers understand the role of machine learning in risk management and how to use machine learning to improve risk management practices.
Auditor
Auditor
Auditors are responsible for examining the financial records of a business to ensure that they are accurate and complete. This course can help auditors understand the role of machine learning in auditing and how to use machine learning to improve audit procedures.
Tax Accountant
Tax Accountant
Tax accountants are responsible for preparing and filing tax returns for businesses and individuals. This course can help tax accountants understand the role of machine learning in tax accounting and how to use machine learning to improve tax accounting practices.
Customer Success Manager
Customer Success Manager
Customer success managers are responsible for ensuring that customers are satisfied with their products or services. This course can help customer success managers understand the role of machine learning in customer success and how to use machine learning to improve customer satisfaction.
For more career information including salaries, visit:
OpenCourser.com/course/9zfcnr/hands
Reading list
We've selected ten 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
Hands-on Machine Learning with AWS and NVIDIA.
Provides a comprehensive introduction to machine learning, covering the fundamental concepts and algorithms, as well as practical implementation in Python. It valuable resource for anyone interested in learning more about machine learning.
Provides a practical introduction to deep learning, covering the fundamental concepts and algorithms, as well as practical implementation in Python. It valuable resource for anyone interested in learning more about deep learning.
Save
Provides a comprehensive overview of computer vision, covering the fundamental concepts and algorithms, as well as practical implementation. It valuable resource for anyone interested in learning more about computer vision.
Provides a comprehensive overview of natural language processing, covering the fundamental concepts and algorithms, as well as practical implementation in Python. It valuable resource for anyone interested in learning more about natural language processing.
Provides a comprehensive overview of machine learning with Python, covering the fundamental concepts and algorithms, as well as practical implementation. It valuable resource for anyone interested in learning more about machine learning with Python.
Provides a practical introduction to machine learning, covering the fundamental concepts and algorithms, as well as practical implementation. It valuable resource for anyone interested in learning more about machine learning.
Provides a comprehensive overview of Python for data analysis, covering the fundamental concepts and algorithms, as well as practical implementation. It valuable resource for anyone interested in learning more about data analysis with Python.
Provides a comprehensive overview of machine learning with R, covering the fundamental concepts and algorithms, as well as practical implementation. It valuable resource for anyone interested in learning more about machine learning with R.
Provides a comprehensive overview of computer vision with Python, covering the fundamental concepts and algorithms, as well as practical implementation. It valuable resource for anyone interested in learning more about computer vision with Python.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a popular Python library for natural language processing. It valuable resource for anyone interested in learning more about natural language processing with NLTK.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9zfcnr/hands
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Time
25 hours
Via
Coursera
Institutions
Amazon Web Services
NVIDIA
Instructors
Anish Mohan
Abhilash Somasamudramath
Antje Barth
Adam Tetelman
Pavan Kumar Sunder
Isaac Privitera
Chris Fregly
Good to know
Teaches learners how to build, train, deploy, and optimize ML workflows with GPU acceleration in Amazon SageMaker
Provides hands-on experience in computer vision and natural language processing use cases
Teaches learners how to prepare a dataset for model training
Emphasizes the use of NVIDIA GPUs for efficient model training and cost-effective model inference hosting
Taught by instructors from AWS, NVIDIA, and industry professionals
Requires prior knowledge of machine learning workflows
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