Professor Myers has been with CAC since 2017, having previously been a member of the research staff of the Bioinformatics Facility of the Institute of Biotechnology (2007-2017) and the Cornell Theory Center (1993-1997, 1998-2007). In addition, Professor Myers is an Adjunct Professor in the Department of Physics at Cornell and a member of the graduate faculty in the fields of physics, computational biology, applied mathematics, and computational science and engineering. Professor Myers works primarily in the field of computational biology, addressing problems in the systems biology of cellular regulation, signaling, metabolism, development, virulence and immunity, as well as in host-pathogen interactions and the spread of infectious diseases on populations, networks, and landscapes.
In this course, you will explore some of the machine learning tools you can use to magnify the analytical power of Python data science programs. You will use the scikit-learn package — a Python package developed for machine learning applications — to develop predictive machine learning models. You will then practice using these models to discover new relationships and patterns in your data. These capabilities allow you to unlock additional value in your data that will aid in making predictions and, in some cases, creating new data.
WHAT YOU'LL LEARN
- Articulate different types of machine learning problems
- Use the Python scikit-learn package to train models and make predictions
- Use the Python scikit-learn package for unsupervised clustering
- Explore a dataset with machine learning
How It Works
3-5 hours per week
100% online, instructor-led
Who Should Enroll
- Data analysts and business analysts
- Database managers
- Technical and systems analysts
- Programmers interested in data science
- Business managers