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, Viviana Marquez is an experienced data scientist who has held positions in a variety of industries, including startups, marketing and advertising, cybersecurity, and streaming services. In addition to her teaching experience in data science for graduate programs and several companies, Viviana holds a Master’s Degree in Data Science from the University of San Francisco and a Bachelor’s degree in Mathematics from Konrad Lorenz University in Bogotá, Colombia. She is passionate about natural language processing, data mining, and data visualization.
Details
Dates: February 22 - April 5
Schedule: Wednesdays, 6pm-9pm
Location: Online
Instructor: Viviana Marquez
Continuing Education Units: 2
Cost: $995 | $248.75 for USF students
Data Institute
San Francisco, CA 94105
Mon-Fri, 9 a.m. - 5 p.m.