Build practical ML skills with Python, Pandas, Sklearn, TensorFlow, and Keras.
Machine learning (ML) is the foundation for many artificial intelligence (AI), and ML algorithms that underlie online shopping recommendations, credit card fraud detection, relevant social media content delivery, rideshare trip pricing, and traffic navigation.
In this course you’ll explore essential ML concepts, tools, and methodology, such as classical and modern algorithms that drive real-world applications such as search engines, image analysis, biometrics, industrial automation, and market segmentation. You’ll work with practical data-driven applications and gain a practical background for creating new products and improving existing ones.
Starting with an introduction to the mathematics underlying ML, we’ll leverage open source Python-based libraries, including Pandas, NumPy, and Sklearn. You’ll improve your intuitive understanding of the underlying algorithms, such as regression, classification, and clustering, as well as related Python-based code samples. You’ll work in a small team or by yourself on a project to present during the final week of class.
For best results in this class, the following topics are highly recommended, some of which are covered in the suggested prerequisite course (listed below):
This class meets simultaneously in a classroom and remotely via Zoom. Students are expected to attend and participate in the course, either in-person or remotely, during the days and times that are specified on the course schedule. Students attending remotely are also strongly encouraged to have their cameras on to get the most out of the remote learning experience. Students attending the class in-person are expected to bring a laptop to each 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.
Programming Tools: Current version of Python with ability to install packages as needed.
