TECH 68 — Machine Learning for Business with Python
Quarter: Spring
Instructor(s): Charlie Flanagan
Date(s): Apr 5—Apr 6
Class Recording Available: No
Class Meeting Day: Saturday and Sunday
Class Meeting Time: 10:00 am—4:00 pm (PT)
Tuition: $435
Refund Deadline: Mar 29
Unit(s): 1
Enrollment Limit: 60
Status: Registration opens Feb 24, 8:30 am (PT)
Quarter: Spring
Day: Saturday and Sunday
Duration: 2 days
Time: 10:00 am—4:00 pm (PT)
Date(s): Apr 5—Apr 6
Unit(s): 1
Tuition: $435
Refund Deadline: Mar 29
Instructor(s): Charlie Flanagan
Enrollment Limit: 60
Recording Available: No
Status: Registration opens Feb 24, 8:30 am (PT)
The availability of open source tools and libraries has made it easier to learn and experiment with AI technologies. These tools, including Python and third-party libraries, are the secret to unlocking the hidden value within the data. In this course, we will explore different business-related problems and solve them using the relevant libraries, including Scikit-Learn, TensorFlow, spaCy, and Altair. We will measure the causal impact of a marketing campaign, predict whether a customer is likely to leave, and determine how much to charge for a new product. Students will learn the entire workflow, from identifying a business problem to gathering the data and implementing the solution in code in a scalable and repeatable way. Students will hear from data science guest speakers tackling these same business problems and leave the course knowing how to formulate business problems in a data science setting and possessing the tools needed to solve them. All students will have the option of working on a capstone project, which can be used as part of a more extensive portfolio.
Python programming experience with Pandas, NumPy, and Matplotlib libraries is recommended but not required.
CHARLIE FLANAGAN
Head of Applied AI, Balyasny Asset Management
Charlie Flanagan is the head of data science at Balyasny Asset Management, a large multistrategy hedge fund. Earlier, he worked for Google, where he was the data science lead for Google Duplex. He received an MS in software engineering from Harvard and an MBA from Columbia. Textbooks for this course:
(Recommended) Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition (ISBN 978-1492032649)