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AWS Instructor
In this machine learning course, you will learn about the machine learning lifecycle, and how to use AWS services at every stage. Additionally, you will discover the diverse sources for machine learning models and learn techniques to evaluate their performance. You will also understand the importance of machine learning operations (MLOps) in streamlining the development and deployment of your machine learning projects.
Register for this course and see more details by visiting:
OpenCourser.com/course/usrnk1/developing
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What's inside
Syllabus
Traffic lights
Traffic lights
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possible dealbreakers
Provides a solid foundation for learners looking to enter the field of machine learning
Offers a comprehensive overview of the machine learning lifecycle, empowering learners to navigate the entire process
Leverages AWS services, providing learners with hands-on experience using industry-standard tools
Emphasizes MLOps best practices, equipping learners to effectively deploy and manage machine learning projects
Teaches diverse model evaluation techniques, fostering a critical understanding of model performance
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Reviews summary
Practical machine learning with aws
According to students, this course is a largely positive resource for learning to deploy machine learning solutions, particularly within the Amazon Web Services (AWS) ecosystem. Learners praise its practical, hands-on approach, with many highlighting the effective use of AWS services like SageMaker for model deployment and the strong focus on Machine Learning Operations (MLOps). While widely considered highly relevant for industry professionals seeking to operationalize ML, some students advise that the course assumes familiarity with core machine learning concepts and Python, suggesting it's less suitable for complete beginners to ML theory. Reviewers note the content is generally up-to-date, effectively teaching real-world applications.
Awareness of potential AWS usage costs for labs.
"Be prepared for some AWS costs if you follow all the labs."
Maintains current relevance despite evolving cloud services.
"The content is current, reflecting recent updates in AWS services."
"I had to troubleshoot a few things due to minor UI changes in AWS since the course was made."
"Some of the older content feels a bit outdated with recent AWS UI changes. It needs regular updates to stay relevant."
Provides valuable insights into streamlining ML operations.
"I learned a lot about MLOps pipelines and deploying models. The labs were particularly useful."
"The MLOps section was a highlight and very relevant for my work. It's concise and to the point."
"The coverage of MLOps was comprehensive enough to get started. Highly relevant for my career."
Highly practical, focusing on ML solution deployment on AWS.
"Excellent course for getting hands-on with SageMaker. I learned a lot about MLOps pipelines and deploying models. The labs were particularly useful."
"Very practical, focusing on the AWS ecosystem... a solid start for anyone wanting to operationalize ML."
"This is exactly what I needed to bridge theory to practical deployment in the AWS cloud."
Beneficial for those with prior ML and Python knowledge.
"I felt it assumes too much prior ML knowledge. As a relative beginner, I struggled with some concepts that weren't explained in depth..."
"I already had some ML background, which helped immensely in following the course."
"Make sure you're comfortable with Python and basic ML concepts beforehand."
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 Developing Machine Learning Solutions with these
activities:
Review linear algebra and calculus
Show steps
Having a strong foundation in linear algebra and calculus will make it easier to understand machine learning concepts.
Browse courses on
Linear Algebra
Show steps
-
Review your notes from previous courses or textbooks
-
Work through practice problems to test your understanding
Attend the 'Machine Learning Meetup' in your city
Show steps
Networking with other machine learning professionals can help you learn about new opportunities and trends.
Browse courses on
Machine Learning
Show steps
-
Find a machine learning meetup in your area
-
Attend the meetup and introduce yourself to other attendees
Form a study group with other students in the course
Show steps
Studying with peers can help you understand the material better and stay motivated.
Browse courses on
Machine Learning
Show steps
-
Find other students in your course who are interested in forming a study group
-
Meet regularly to discuss the course material and work on assignments together
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review 'Machine Learning: A Probabilistic Perspective' by Kevin Murphy
Show steps
Reviewing this foundational book can help provide a deeper understanding of probabilistic machine learning concepts.
View
Probabilistic Machine Learning: Advanced Topics...
on Amazon
Show steps
-
Read chapters 1-3 to gain an introduction to probabilistic machine learning
-
Work through the exercises in chapter 4 to practice applying the concepts
Follow the 'Machine Learning Specialization' on Coursera
Show steps
This specialization will provide a comprehensive overview of machine learning concepts and techniques.
Browse courses on
Machine Learning
Show steps
-
Complete the four courses in the specialization
-
Participate in the discussion forums to ask questions and share your insights
Complete the 'Machine Learning Practice Problems' by Sebastian Raschka
Show steps
This book provides a comprehensive set of practice problems to help you master machine learning algorithms.
Browse courses on
Machine Learning
Show steps
-
Solve at least 20 problems from each chapter
-
Focus on understanding the underlying concepts rather than just memorizing solutions
Build a machine learning model to predict customer churn
Show steps
This project will give you hands-on experience in applying machine learning to a real-world problem.
Browse courses on
Machine Learning
Show steps
-
Collect and prepare the data
-
Train and evaluate different machine learning models
-
Deploy the model and track its performance
Create a blog post or presentation on a machine learning project
Show steps
This assignment will help you synthesize your knowledge and improve your communication skills.
Browse courses on
Machine Learning
Show steps
-
Choose a machine learning project that you have worked on
-
Write a blog post or create a presentation that describes the project, the techniques you used, and the results you achieved
Review linear algebra and calculus
Show steps
Having a strong foundation in linear algebra and calculus will make it easier to understand machine learning concepts.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review your notes from previous courses or textbooks
- Work through practice problems to test your understanding
Attend the 'Machine Learning Meetup' in your city
Show steps
Networking with other machine learning professionals can help you learn about new opportunities and trends.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a machine learning meetup in your area
- Attend the meetup and introduce yourself to other attendees
Form a study group with other students in the course
Show steps
Studying with peers can help you understand the material better and stay motivated.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find other students in your course who are interested in forming a study group
- Meet regularly to discuss the course material and work on assignments together
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review 'Machine Learning: A Probabilistic Perspective' by Kevin Murphy
Show steps
Reviewing this foundational book can help provide a deeper understanding of probabilistic machine learning concepts.
View
Probabilistic Machine Learning: Advanced Topics...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to gain an introduction to probabilistic machine learning
- Work through the exercises in chapter 4 to practice applying the concepts
Follow the 'Machine Learning Specialization' on Coursera
Show steps
This specialization will provide a comprehensive overview of machine learning concepts and techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Complete the four courses in the specialization
- Participate in the discussion forums to ask questions and share your insights
Complete the 'Machine Learning Practice Problems' by Sebastian Raschka
Show steps
This book provides a comprehensive set of practice problems to help you master machine learning algorithms.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Solve at least 20 problems from each chapter
- Focus on understanding the underlying concepts rather than just memorizing solutions
Build a machine learning model to predict customer churn
Show steps
This project will give you hands-on experience in applying machine learning to a real-world problem.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Collect and prepare the data
- Train and evaluate different machine learning models
- Deploy the model and track its performance
Create a blog post or presentation on a machine learning project
Show steps
This assignment will help you synthesize your knowledge and improve your communication skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a machine learning project that you have worked on
- Write a blog post or create a presentation that describes the project, the techniques you used, and the results you achieved
Career center
Learners who complete Developing Machine Learning Solutions will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/usrnk1/developing
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