Our tool of choice to build the machine learning model was Amazon SageMaker. We landed on unsupervised learning for our technique because our data was not labeled and K-means as the learning algorithm because we needed to group our students into a specific number (or k number) of clusters (or groups) based on similarities. We chose our technique based on the accessible data points and the learning algorithm based on the results we expected (i.e. There are several machine learning techniques (supervised, unsupervised, reinforcement, transfer, etc.) and learning algorithms to choose from. The key to making this solution work was our chosen machine learning technique that allowed us to group (or cluster) like students.
Tip: To learn more about machine learning, watch this episode of Kesha’s Korner to come up to speed. If you’re not familiar with AWS machine learning, it allows a computer to study data and find trends and patterns that may otherwise be hidden. Our overall hope is that a machine learning-enabled recommendation engine will increase the viewership of courses and student engagement since we’re recommending courses specifically tailored to your interests and mastery. Why consider building a course recommendation engine in the first place? Well, for the personal learning challenge, of course!Īlong with personal growth and development, A Cloud Guru recently launched a new combined platform with Linux Academy offering 250% more courses than before, 470+ quizzes and exams, and 1,500+ Hands-On Labs! How are you going to navigate all of that content? A machine learning-enabled recommendation engine could be just the tool to help you. It’s really cool! The Benefits of a Course Recommendation Engine During this adventure, we learned a lot and produced a proof of concept (POC) for an engine that recommends courses to students using machine learning! Our POC recommends titles to you based on what you’ve watched and mastered along with what others in your assigned learning cluster have watched and mastered. To begin this learning adventure, I teamed up with my colleague Julie Elkins to solve this challenge using machine learning and Amazon SageMaker. Luckily clustering, a machine learning technique, can be used to classify each student in a specific group. The only missing component was an easy way to compare students to determine if they have similar tastes. We track viewing history and metadata about our courses. These same principles could easily be applied to make course recommendations. Netflix makes recommendations based on your viewing history, what other members with similar tastes watch, and metadata about the movies - like genre, categories, and more. The Benefits of a Course Recommendation EngineĪ Matter of Taste: Machine Learning on Amazon Web Services (AWS).