Hands-on Machine Learning with AWS and NVIDIA from Coursera

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.

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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

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

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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

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Find a workshop
Attend the workshop
Participate in the exercises and discussions

Follow tutorial on Amazon SageMaker

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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

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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

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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

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Review notes from a previous course
Find online resources for reviewing linear algebra
Do practice problems to test for understanding

Five other activities

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Review the book 'Deep Learning with Python'

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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

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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

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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.

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Find a study group
Participate in group discussions
Review and discuss the material covered in class

Practice deploying a model on Amazon SageMaker

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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.

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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

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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

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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

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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

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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 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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning 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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Product 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.

See salaries and explore the career path for Marketing 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.

See salaries and explore the career path for Sales Manager

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.

See salaries and explore the career path for Compliance Officer

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.

See salaries and explore the career path for Financial Analyst

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.

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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.

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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.

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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.

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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.

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