The Center for Data and Bioinformation Services (CDABS) is the University of Maryland Health Sciences and Human Services Library hub for data and bioinformation learning, services, resources, and communication.
We are wrapping up another week (Mar 08 -12) of learning and growing at CDABS. This week’s DABS focus will be on machine learning. What is machine learning? Machine learning involves using specialized computer software for automation and decisions by a person to extract knowledge from data. There are two main categories of Machine learning, supervised learning which involves making predictions using data (for example: spam filters) and unsupervised learning for finding a structure from data (Topic Modeling, in Natural Language Processing, to elucidate topics in a collection of texts). There are several resources on the web to get started doing machine learning in your research. Here are a few places to start digging in that are phenomenal.
- Data School is an online portal with blog posts, videos, courses, Jupyter notebooks, and webcast recordings to learn data science. Data School offers three Machine Learning courses: Introduction to Machine Learning with scikit-learn, Building an Effective Machine Learning Workflow with scikit-learn, and Machine Learning with Text in Python. Learn more here: (10 minute read) https://www.dataschool.io/ml-courses/
- Machine Learning Mastery is a website dedicated to making you awesome at machine learning. They use a top down approach to learn modern machine learning via hands-on tutorials. (15 minute read) https://machinelearningmastery.com/start-here/
- StatQuest provides an “An epic journey through statistics and machine learning”. Join Josh Starmer and see his unique and fun approach to breaking down the complex topics into small digestible bits via engaging YouTube videos with accompanying code. Check out the section on machine learning but also make sure to broaden your exploration to many other topics in statistics. (10 minute read) https://statquest.org/video-index/#machine
Contact: Amy Yarnell, Data Services Librarian and Jean-Paul Courneya, Bioinformationist – at email@example.com.
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