This course provides a quick review and summary of what was learned during the Introduction to Machine Learning on AWS Learning Path.
Congratulations on completing the "Introduction to Machine Learning on AWS" Learning Path!
We hope you found this content both informative and engaging. Before we finish, we'll provide a quick recap of the concepts we've covered.
We introduced you to basic Machine Learning Concepts. We discussed the differences between supervised learning and unsupervised learning. We talked about the different types of problems machine learning can be used to solve. For example, problems related to regression, or classification, or clustering, etc.
We reviewed several of the most common and popular Machine Learning algorithms. Then we provided the basic introduction to deep learning and deep Neural Networks.
We then provided you with an introduction to the Amazon Machine Learning service, and how it can be used to guide you through the process of creating Machine Learning models without having to learn complex Machine Learning algorithms and/or technology.
Next, you had the opportunity to perform a hands-on lab using the Amazon Machine Learning service. Creating a predictive model for Forecasting Flight Delays. Following on from this, another hands-on lab was provided. To highlight the importance of leveraging GPUs for machine learning.
And finally, you are able to build and train your very own MXNet Neural Network to Style Images. And another of their labs.
Okay, so that brings this learning path to a close. We suggest you perform a final assessment exam if you haven't done so already. This will give you an idea as to which areas you might need to continue or review. Please feel free to contact us at firstname.lastname@example.org if you have any questions, and/or comments.
Congratulations again on completing this Learning Path.
You're well on your way to mastering Machine Learning on the AWS platform!
Jeremy is a Content Lead Architect and DevOps SME here at Cloud Academy where he specializes in developing DevOps technical training documentation.
He has a strong background in software engineering, and has been coding with various languages, frameworks, and systems for the past 25+ years. In recent times, Jeremy has been focused on DevOps, Cloud (AWS, Azure, GCP), Security, Kubernetes, and Machine Learning.
Jeremy holds professional certifications for AWS, Azure, GCP, Terraform, Kubernetes (CKA, CKAD, CKS).