Build data-driven AI solutions using Python, statistics, and machine learning techniques.
This course introduces students to the Python programming language essential for data manipulation, statistical analysis, and predictive modeling techniques required for machine learning and artificial intelligence.
We will explore the wonderfully concise and expressive use of Python's advanced module features and apply it in probability, statistical analysis, training models, and various other applications. Students will explore mathematical operations with array data structures, optimization, probability density function, interpolation, visualization, and other high-performance benefits of core scientific packages such as NumPy, Pandas, scikit-learn, and Matplotlib.
Additionally, students will learn modern machine learning concepts and techniques, including supervised, unsupervised, and semi-supervised learning, to develop predictive models using Python libraries. The course concludes with a real-world, end-to-end machine learning project, providing students with practical experience in solving challenging problems.
Additional Information
AI* - This course uses Generative AI through hands-on labs, to develop the skills needed to implement and evaluate ML models effectively.
Skills Needed:
| Date | Start Time | End Time | Meeting Type | Location |
|---|---|---|---|---|
| Mon, 06-29-2026 | 6:00pm | 9:00pm | Live-Online | REMOTE |
| Mon, 07-06-2026 | 6:00pm | 9:00pm | Live-Online | REMOTE |
| Mon, 07-13-2026 | 6:00pm | 9:00pm | Live-Online | REMOTE |
| Mon, 07-20-2026 | 6:00pm | 9:00pm | Live-Online | REMOTE |
| Mon, 07-27-2026 | 6:00pm | 9:00pm | Live-Online | REMOTE |
| Mon, 08-03-2026 | 6:00pm | 9:00pm | Live-Online | REMOTE |
| Mon, 08-10-2026 | 6:00pm | 9:00pm | Live-Online | REMOTE |
| Mon, 08-17-2026 | 6:00pm | 9:00pm | Live-Online | REMOTE |
| Mon, 08-24-2026 | 6:00pm | 9:00pm | Live-Online | REMOTE |
| Mon, 08-31-2026 | 6:00pm | 9:00pm | Live-Online | REMOTE |
This class is offered in an online synchronous format. Students are expected to log into this course via Canvas at the start time of scheduled meetings and participate via Zoom, for the duration of each scheduled class meeting.
To see all meeting dates, click "Full Schedule" below.
You will be granted access in Canvas to your course site and course materials approximately 24 hours prior to the published start date of the course.
Required Tools & Materials:
Students are expected to have computers with Python 3.x, Jupyter Notebooks, and libraries: Pandas, Matplotlib and Numpy installed. Installing the Anaconda distribution of Python, gives access to Jupyter Notebooks and all the required libraries. Free Individual Edition can be obtained from: https://www.anaconda.com/products/individual
Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems; Aurélien Géron; O'Reilly Media Inc.; 2025. ISBN: 979-8341607989
Recommended Textbooks:
Python Data Science Handbook; Jake VanderPlas; O'Reilly Media Inc.; 2023. ISBN: 9781098121228. Available at: https://jakevdp.github.io/PythonDataScienceHandbook/
Machine Learning with Python Cookbook; Gallatin and Albon; O'Reilly Media Inc.; 2023. ISBN: 9781098135690. Available at: https://learning.oreilly.com/library/view/machine-learning-with/9781098…;
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, O'Reilly Media, Inc., 2022-10-04, ISBN: 9781098122478.
Students can use this textbook as an alternative to the required textbook. Available at O'Reilly for Public Libraries.
|| Skills Needed:
Prerequisites / Skills Needed
