TECH 27 — Applied Machine Learning with Python
Quarter: Summer
Instructor(s): Ishaani Priyadarshini
Date(s): Jul 9—Aug 27
Class Recording Available: Yes
Class Meeting Day: Wednesdays
Grade Restriction: No letter grade
Class Meeting Time: 6:00—7:30 pm (PT)
Tuition: $460
Refund Deadline: Jul 11
Unit(s): 1
Enrollment Limit: 45
Status: Open
Quarter: Summer
Day: Wednesdays
Duration: 8 weeks
Time: 6:00—7:30 pm (PT)
Date(s): Jul 9—Aug 27
Unit(s): 1
Tuition: $460
Refund Deadline: Jul 11
Instructor(s): Ishaani Priyadarshini
Grade Restriction: No letter grade
Enrollment Limit: 45
Recording Available: Yes
Status: Open
Machine learning has evolved from a technical specialty into an essential decision-making tool for business leaders. This course uses Python to equip professionals with both technical skills and strategic frameworks for effective decision-making. Through hands-on exercises, you'll master essential techniques in regression, classification, and advanced algorithms in deep learning. Students will implement and test over 15 different machine learning methods, gaining practical experience through real-world case studies in finance, healthcare, ecommerce, and marketing and interactive projects selected to reflect real-world business challenges. We will explore both supervised and unsupervised learning techniques, with assignments tailored to accommodate varying experience levels. Students will complete a customizable final project that aligns with their professional goals.
ISHAANI PRIYADARSHINI
Data Science Course Facilitator, Cornell
Ishaani Priyadarshini received a PhD in electrical and computer engineering from the University of Delaware, where she focused on technological singularity. She completed postdoctoral research at UC Berkeley, exploring trustworthy and fair AI systems. She has taught at UC Berkeley's School of Information and was a course facilitator for Cornell’s online certificate programs known as eCornell. Her current work includes developing innovative courses in data science and AI.
Textbooks for this course:
There are no required textbooks; however, some fee-based online readings may be assigned.