This course is the first of five classes in the Machine Learning University and introduces core concepts and tools needed to work on any machine learning (ML) problem. In this class, students will gain a practical introduction to the core concepts of ML, along with the ability to apply them to the most common datasets. They will see key ML modeling methods, including regression and tree-based methods. They will also learn how to measure fairness and bias and follow best practices for responsible machine learning/artificial intelligence (AI).
Course Dates:
- Start: Tuesday, October 3, 2023
- End: Thursday, December 14, 2023
Machine learning is one of the most active areas of growth in modern technology. Thanks to recent advances, it is now easier than ever to build machine learning powered solutions to problems. This course provides a hands-on, application-driven introduction to ML.
- The course introduces the core concepts and tools needed to work on any ML problem. By combining industry-standard tools like Jupyter and Amazon Sagemaker Notebooks with the open source AutoGluon AutoML library, students will obtain a practical introduction to the core concepts of ML, along with the ability to apply them to the most common datasets: those that can be stored in spreadsheet-like tables. With the help of real-world examples, students will understand the key aspects of successful ML solutions and why some ML applications are more successful than others.
- The course dives into how ML technology works by introducing key ML modeling methods, most notably regression and tree-based methods. With introductions to feature engineering, hyperparameter tuning, and assembling, students will learn how to combine and optimize different ML methods to provide the most accurate predictions possible.
- The course introduces the best practices for responsible ML/AI. After a discussion on how to measure fairness and bias, the course turns to a series of hands-on examples of how to mitigate bias in ML models at various stages of model development.
- By the end of the course, students will have a solid foundation in machine learning and the skills necessary to build and deploy ML models in real-world settings.
Audience:
This intermediate course is intended for students who are in a degree program related to computer science, statistics, and data science, or industry professionals interested in professional development and career advancement.
Recommended Prerequisites:
Completion of the Python Fundamentals Boot Camp is required prior to this class.
This course requires an understanding of cloud computing concepts, linear algebra, statistical probability, and coding (Python or equivalent).
Information Sessions:
- Thursday, August 10: 6:00 p.m. – 7:00 p.m.
- Saturday, August 19: 1:00 p.m. – 2:00 p.m.
- Thursday, August 24: 12:00 p.m. – 1:00 p.m.
Please use this link to sign up to attend an information session where you will learn more about the class from industry tech leaders:
https://forms.office.com/r/pamWHGqjSp
Cost:
This course will be paid for through a government-funded grant and will be offered to students at no cost.
Class runs 10/3 – 12/14.
