Introduction to Machine Learning
This course builds an essential toolkit for anyone starting out in ML or data science. Foundational issues in this area, such as cross-validation and the bias-variance trade-off, are covered with a focus on the intuition behind their use. This course also explores the principal techniques that any machine learner or data scientist should know including logistic regression, decision trees, classification, and clustering.
Upon completion of the certificate, participants will be able to:
- Define key machine learning terminology;
- Apply common machine learning models to elementary data sets;
- Properly assess models using validation and test sets with appropriate metrics;
- Clean and perform simple feature engineering to improve model performance; and
- Be familiar with the key libraries: Matplotlib, Pandas, Numpy, and scikit-learn.
Participants are expected to be familiar with Python fundamentals, with basic statistics knowledge being helpful but not required.
The course instructor Tim Lee is a staff machine learning engineer for workday HR systems for the last 4 years. Before this he has worked in technical data science positions in finance, oil and gas, cyber security, and marketing. He has two master's degrees from UCLA and USF, and has experience teaching in university, certificate, and consulting contexts.
Details
Dates: TBD
Schedule: BD
Location: Online
Instructor: Tim Lee
Cost: $1195 - $795 USF Alumni - $295 USF Students
Data Institute
San Francisco, CA 94105
Mon-Fri, 9 a.m. - 5 p.m.