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Blaine Sundrud
Machine learning (ML) is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market. The World Economic Forum states the growth of artificial intelligence (AI) could create 58 million net new jobs in the next few...
Read more
Machine learning (ML) is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market. The World Economic Forum states the growth of artificial intelligence (AI) could create 58 million net new jobs in the next few...
Read more
Machine learning (ML) is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market. The World Economic Forum states the growth of artificial intelligence (AI) could create 58 million net new jobs in the next few years, yet it’s estimated that currently there are 300,000 AI engineers worldwide, but millions are needed. This means there is a unique and immediate opportunity for you to get started with learning the essential ML concepts that are used to build AI applications – no matter what your skill levels are. Learning the foundations of ML now, will help you keep pace with this growth, expand your skills and even help advance your career.
This course will teach you how to get started with AWS Machine Learning. Key topics include: Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP) on AWS. Each topic consists of several modules deep-diving into variety of ML concepts, AWS services as well as insights from experts to put the concepts into practice.
Register for this course and see more details by visiting:
OpenCourser.com/course/8iujak/getting
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Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Appropriate for those new to the field
Appropriate for those interested in using Machine Learning on AWS
Appropriate for intermediate learners looking to strengthen their Machine Learning foundation
Appropriate for anyone interested in advancing their career with machine learning skills
Appropriate for individuals looking to develop professional skills in Machine Learning
May not be appropriate if you are a ML expert already
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Reviews summary
Aws ml trailblazer
The "Getting Started with AWS Machine Learning" course is an introductory-level course that provides a broad overview of the field of machine learning and how it can be implemented using Amazon Web Services (AWS). The course covers a wide range of topics, from the basics of machine learning to more advanced topics such as natural language processing and computer vision. The course is well-structured and easy to follow, and the instructors are knowledgeable and engaging. Overall, the course is a great way to learn about machine learning and how to use AWS to implement machine learning solutions.
The course is well-structured and easy to follow, with each week building on the previous one.
"The course is very informative and give the basics of machine learning and how aws uses machine learning for various services like amazon rekongition."
The course provides a comprehensive overview of the various AWS services and products that can be used for machine learning, including Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.
"This course gave me a good introduction to Machine Learning and helped me understand AWS service offerings around AI and ML."
"This course taught me the process of ML model creation in AWS. I learned the basics of ML algorithm, training, creating endpoints etc. Thank you for sharing all the info."
The instructors are knowledgeable and engaging, and they do a good job of explaining complex concepts in a clear and concise way.
"The course was excellent. Was able to see new aspects of tools I've heard of before. This course helped me decide which tools I would like to move forward with for my web application."
The course provides limited hands-on experience, which can make it difficult to apply the concepts learned in the course to real-world projects.
"This is a very good course for beginners, every aspect is explained fluently and the flow of teaching is great. Thank a lot amazon."
"This course is a compilation of unstructured content that has been taken from the actual AWS certification programs."
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 Getting Started with AWS Machine Learning with these
activities:
Connect with AWS Experts
Show steps
Seek guidance and support from experienced AWS professionals to enhance your learning.
Show steps
-
Attend AWS webinars and meetups.
-
Join AWS online communities and forums.
-
Reach out to AWS experts on LinkedIn or Twitter.
Review Intro to ML concepts
Show steps
This will help you build a stronger Machine Learning foundation before the course begins.
Show steps
-
Review the basics of linear algebra and calculus.
-
Learn about different types of machine learning algorithms.
-
Understand the basics of supervised and unsupervised learning.
Review AWS core services
Show steps
Brush up on your understanding of AWS Core services to strengthen your foundation for this course on AWS Machine Learning.
Show steps
-
Read the AWS documentation on core services.
-
Complete the AWS Cloud Quest.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Attend an AWS Machine Learning Workshop
Show steps
Attend an online or in-person workshop from AWS to supplement course material and gain hands-on experience with AWS machine learning services.
Browse courses on
AWS Machine Learning
Show steps
-
Search for upcoming AWS Machine Learning workshops.
-
Register for a workshop that fits your schedule and interests.
-
Attend the workshop and actively participate.
Review basic python programming skills
Show steps
This course requires a basic understanding of Python. If you have not learned Python before, consider refreshing your skills.
Browse courses on
Python
Show steps
-
Take an online tutorial on Python basics.
-
Complete coding exercises on a platform like leetcode or hackerrank.
Build a Recommendation System
Show steps
Completing this tutorial will help you gain practical experience in applying machine learning to build recommendation systems, a common use case in various industries.
Browse courses on
Recommendation Systems
Show steps
-
Follow a tutorial on building a recommendation system using an ML framework.
-
Gather a dataset and prepare it for training.
-
Select and train an appropriate machine learning model.
-
Evaluate the performance of your model.
-
Deploy the model and serve recommendations.
Complete AWS ML tutorials
Show steps
Engage with tutorials to practice applying ML concepts in an AWS context.
Browse courses on
AWS Machine Learning
Show steps
-
Follow the 'Getting Started with AWS Machine Learning' tutorial.
-
Complete the 'Build an Image Classification Model with Amazon SageMaker' tutorial.
-
Work through the 'Deploy a Trained Model to an Amazon SageMaker Endpoint' tutorial.
Follow the AWS Machine Learning Tutorials
Show steps
Deepen your understanding of AWS Machine Learning by following the official AWS tutorials.
Browse courses on
AWS Machine Learning
Show steps
-
Choose a tutorial that aligns with your interests.
-
Follow the tutorial steps and complete the exercises.
Solve Leetcode problems
Show steps
Solving Leetcode problems can help solidify your understanding of machine learning concepts and improve your problem-solving skills.
Show steps
-
Pick a problem that aligns with a course topic.
-
Read the problem statement carefully.
-
Come up with an optimal algorithm.
-
Implement your solution in code.
-
Submit your solution and check the results.
Solve AWS Machine Learning Practice Problems
Show steps
Test and strengthen your AWS Machine Learning skills by solving practice problems.
Browse courses on
AWS Machine Learning
Show steps
-
Find practice problems online or in resources provided by AWS.
-
Attempt to solve the problems on your own.
-
Review your solutions and identify areas for improvement.
Solve ML coding challenges
Show steps
Sharpen your ML coding skills by working through practice problems.
Show steps
-
Solve coding challenges on platforms like LeetCode or HackerRank.
-
Participate in online ML coding competitions.
-
Build your own small ML projects.
Create a Machine Learning Project
Show steps
Working on a machine learning project will allow you to apply the concepts learned in the course and create something tangible that demonstrates your skills.
Show steps
-
Identify a problem or task that can be solved with machine learning.
-
Gather and prepare a dataset for your project.
-
Explore and select appropriate machine learning algorithms.
-
Train and evaluate your models.
-
Deploy your model and create a user-facing application.
Build a Machine Learning Model on AWS
Show steps
Solidify your learning by building a hands-on machine learning model using AWS services.
Browse courses on
AWS Machine Learning
Show steps
-
Define the problem you want to solve.
-
Gather and prepare your data.
-
Choose and train a machine learning model.
-
Deploy your model and evaluate its performance.
Participate in AWS Machine Learning Hackathon
Show steps
Put your skills to the test and collaborate with others to solve real-world machine learning challenges.
Browse courses on
AWS Machine Learning
Show steps
-
Find an AWS Machine Learning hackathon.
-
Form a team or join an existing one.
-
Develop and submit your solution.
Connect with AWS Experts
Show steps
Seek guidance and support from experienced AWS professionals to enhance your learning.
Show steps
Show steps
Show steps
- Attend AWS webinars and meetups.
- Join AWS online communities and forums.
- Reach out to AWS experts on LinkedIn or Twitter.
Review Intro to ML concepts
Show steps
This will help you build a stronger Machine Learning foundation before the course begins.
Show steps
Show steps
Show steps
- Review the basics of linear algebra and calculus.
- Learn about different types of machine learning algorithms.
- Understand the basics of supervised and unsupervised learning.
Review AWS core services
Show steps
Brush up on your understanding of AWS Core services to strengthen your foundation for this course on AWS Machine Learning.
Show steps
Show steps
Show steps
- Read the AWS documentation on core services.
- Complete the AWS Cloud Quest.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Attend an AWS Machine Learning Workshop
Show steps
Attend an online or in-person workshop from AWS to supplement course material and gain hands-on experience with AWS machine learning services.
Browse courses on
AWS Machine Learning
Show steps
Show steps
Show steps
- Search for upcoming AWS Machine Learning workshops.
- Register for a workshop that fits your schedule and interests.
- Attend the workshop and actively participate.
Review basic python programming skills
Show steps
This course requires a basic understanding of Python. If you have not learned Python before, consider refreshing your skills.
Browse courses on
Python
Show steps
Show steps
Show steps
- Take an online tutorial on Python basics.
- Complete coding exercises on a platform like leetcode or hackerrank.
Build a Recommendation System
Show steps
Completing this tutorial will help you gain practical experience in applying machine learning to build recommendation systems, a common use case in various industries.
Browse courses on
Recommendation Systems
Show steps
Show steps
Show steps
- Follow a tutorial on building a recommendation system using an ML framework.
- Gather a dataset and prepare it for training.
- Select and train an appropriate machine learning model.
- Evaluate the performance of your model.
- Deploy the model and serve recommendations.
Complete AWS ML tutorials
Show steps
Engage with tutorials to practice applying ML concepts in an AWS context.
Browse courses on
AWS Machine Learning
Show steps
Show steps
Show steps
- Follow the 'Getting Started with AWS Machine Learning' tutorial.
- Complete the 'Build an Image Classification Model with Amazon SageMaker' tutorial.
- Work through the 'Deploy a Trained Model to an Amazon SageMaker Endpoint' tutorial.
Follow the AWS Machine Learning Tutorials
Show steps
Deepen your understanding of AWS Machine Learning by following the official AWS tutorials.
Browse courses on
AWS Machine Learning
Show steps
Show steps
Show steps
- Choose a tutorial that aligns with your interests.
- Follow the tutorial steps and complete the exercises.
Solve Leetcode problems
Show steps
Solving Leetcode problems can help solidify your understanding of machine learning concepts and improve your problem-solving skills.
Show steps
Show steps
Show steps
- Pick a problem that aligns with a course topic.
- Read the problem statement carefully.
- Come up with an optimal algorithm.
- Implement your solution in code.
- Submit your solution and check the results.
Solve AWS Machine Learning Practice Problems
Show steps
Test and strengthen your AWS Machine Learning skills by solving practice problems.
Browse courses on
AWS Machine Learning
Show steps
Show steps
Show steps
- Find practice problems online or in resources provided by AWS.
- Attempt to solve the problems on your own.
- Review your solutions and identify areas for improvement.
Solve ML coding challenges
Show steps
Sharpen your ML coding skills by working through practice problems.
Show steps
Show steps
Show steps
- Solve coding challenges on platforms like LeetCode or HackerRank.
- Participate in online ML coding competitions.
- Build your own small ML projects.
Create a Machine Learning Project
Show steps
Working on a machine learning project will allow you to apply the concepts learned in the course and create something tangible that demonstrates your skills.
Show steps
Show steps
Show steps
- Identify a problem or task that can be solved with machine learning.
- Gather and prepare a dataset for your project.
- Explore and select appropriate machine learning algorithms.
- Train and evaluate your models.
- Deploy your model and create a user-facing application.
Build a Machine Learning Model on AWS
Show steps
Solidify your learning by building a hands-on machine learning model using AWS services.
Browse courses on
AWS Machine Learning
Show steps
Show steps
Show steps
- Define the problem you want to solve.
- Gather and prepare your data.
- Choose and train a machine learning model.
- Deploy your model and evaluate its performance.
Participate in AWS Machine Learning Hackathon
Show steps
Put your skills to the test and collaborate with others to solve real-world machine learning challenges.
Browse courses on
AWS Machine Learning
Show steps
Show steps
Show steps
- Find an AWS Machine Learning hackathon.
- Form a team or join an existing one.
- Develop and submit your solution.
Career center
Learners who complete Getting Started with AWS Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists develop, implement, test, and maintain mathematical and statistical models and algorithms that leverage data to make informed decisions. By using this course to develop their knowledge of Machine Learning, Data Scientists can enhance their ability to identify trends, patterns, and insights in data which can be valuable to companies. This course will also help you prepare for the certification required for this role: the Associate Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, test, and deploy machine learning models and systems. The material on Computer Vision and NLP in particular may be helpful for someone in this role. This course can help you build a foundation in these areas, which can be helpful for ML Engineers that wish to specialize in CV or NLP.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop artificial intelligence systems, algorithms, and solutions. By learning about ML on AWS, they can improve their understanding of the fundamentals of artificial intelligence. This course may also help prepare AI Engineering candidates for the Machine Learning Specialty certification.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts translate data into insights. They collect, analyze, interpret, and present data to help businesses make informed decisions. The material on Computer Vision and NLP may be particularly relevant to Business Intelligence Analysts that work in industries where these technologies are widely used; for example, the financial and healthcare industries.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, analyzing, interpreting, and visualizing data. While this course does not directly cover Data Analysis, data analysts may find the sections on Computer Vision and NLP to be particularly helpful.
Product Manager (Machine Learning)
Product Manager (Machine Learning)
Product Managers: Machine Learning are responsible for the development and management of machine learning products. This course may be useful for those that wish to pivot into this role, especially for individuals without a background in ML.
Research Scientist: Machine Learning
Research Scientist: Machine Learning
Research Scientists: Machine Learning research and develop new machine learning algorithms and techniques to advance the field. This course may be useful for those that wish to pivot into this role, especially for individuals without a background in ML.
Systems Engineer: Machine Learning
Systems Engineer: Machine Learning
Systems Engineers: Machine Learning design and implement machine learning systems. This course may be useful for those that wish to pivot into this role, especially for individuals without a background in ML.
Software Engineer (Machine Learning)
Software Engineer (Machine Learning)
Software Engineers: Machine Learning develop and maintain software systems that leverage machine learning models. This course may be useful for those that wish to pivot into this role, especially for individuals without a background in ML.
Market Research Analyst
Market Research Analyst
Market Research Analysts conduct research on markets, customers, and competitors to help businesses make informed decisions. While this course does not directly cover Market Research, those who wish to specialize in using machine learning for market research may find the material on Computer Vision and NLP to be helpful.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts develop and implement mathematical and statistical models to analyze financial data. While this course does not directly cover Quantitative Analysis, those who wish to specialize in applying ML techniques for financial data may find the material on Computer Vision and NLP to be helpful.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts apply mathematical and analytical techniques to improve the efficiency of systems. This course may be useful for those that wish to specialize in leveraging machine learning to improve operational efficiency.
Data Engineer: Machine Learning
Data Engineer: Machine Learning
Data Engineers: Machine Learning design and build data pipelines for machine learning models. This course may be useful for those that wish to pivot into this role, especially for individuals without a background in ML.
Machine Learning Architect
Machine Learning Architect
Machine Learning Architects design and implement the architecture for machine learning systems. This course may be useful for those that wish to pivot into this role, especially for individuals without a background in ML.
Technical Writer: Machine Learning
Technical Writer: Machine Learning
Technical Writers: Machine Learning write and edit technical documentation for machine learning products and services. This course may be useful for those that wish to pivot into this role, especially for individuals without a background in ML.
For more career information including salaries, visit:
OpenCourser.com/course/8iujak/getting
Reading list
We've selected 13 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
Getting Started with AWS Machine Learning.
Save
Provides a comprehensive and in-depth coverage of deep learning concepts and algorithms. It valuable resource for learners who want to specialize in deep learning and its applications.
Provides a practical and hands-on approach to machine learning. It valuable resource for learners who want to learn how to build and deploy machine learning models.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It valuable resource for learners who want to specialize in computer vision and its applications.
Provides practical, hands-on experience with machine learning using popular libraries like Scikit-Learn, Keras, and TensorFlow. It is an excellent resource for learners who want to apply machine learning techniques to real-world problems.
Provides a practical and hands-on approach to deep learning using the Python programming language. It valuable resource for learners who want to learn how to build and deploy deep learning models.
Provides a comprehensive overview of natural language processing techniques and applications. It valuable resource for learners who want to specialize in natural language processing and its applications.
Provides a practical and hands-on approach to natural language processing. It valuable resource for learners who want to learn how to build and deploy natural language processing models.
Provides a comprehensive and foundational overview of artificial intelligence concepts and techniques. It valuable resource for learners who want to understand the history, theory, and applications of artificial intelligence.
Provides a comprehensive overview of machine learning algorithms and their applications. It valuable resource for learners who want to understand the different types of machine learning algorithms and how to use them.
Provides a comprehensive and foundational overview of reinforcement learning concepts and techniques. It valuable resource for learners who want to understand the theory and applications of reinforcement learning.
Provides a comprehensive introduction to machine learning concepts, algorithms, and techniques. It valuable resource for beginners who want to understand the fundamentals of machine learning.
Provides a comprehensive overview of machine learning techniques and applications using the R programming language. It valuable resource for learners who want to use R for machine learning tasks.
Provides a probabilistic perspective on machine learning concepts and algorithms. It valuable resource for learners who want to understand the theoretical foundations of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8iujak/getting
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Time
24 hours
Via
Coursera
Institution
Amazon Web Services
Instructor
Blaine Sundrud
Good to know
Appropriate for those new to the field
Appropriate for those interested in using Machine Learning on AWS
Appropriate for intermediate learners looking to strengthen their Machine Learning foundation
Appropriate for anyone interested in advancing their career with machine learning skills
Appropriate for individuals looking to develop professional skills in Machine Learning
May not be appropriate if you are a ML expert already
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