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CS 08 W — Machine Learning with Python

Quarter: Winter
Course Format: Flex Online (About Formats)
Duration: 10 weeks
Date(s): Jan 10—Mar 18
Refund Deadline: Jan 13
Units: 2
Tuition: $625
Instructor(s): Michael Galarnyk
Limit: 45
Class Recording Available: Yes
Status: Open
DOWNLOAD THE SYLLABUS » (subject to change)
Flex Online(About Formats)
Jan 10—Mar 18
10 weeks
Refund Date
Jan 13
2 Units
Michael Galarnyk
DOWNLOAD THE SYLLABUS » (subject to change)
Utilizing machine learning to apply algorithms to their data has helped companies maximize efficiencies, pursue new markets, and create new products. This trend has prompted many industries to recognize the value of machine learning, creating a high demand for knowledge in this field. This course will cover machine learning foundations and some of the leading open source tools in Python. We will start by learning the various strengths and weaknesses of various machine learning algorithms and then apply them to real-world situations. Additionally, we will touch on use cases where deep learning is appropriate, such as image classification and natural language processing. We will use the Python data science ecosystem to perform machine learning. These tools are open source and popular among data scientists in both academia and industry. The tools we will use include the Jupyter Notebook, pandas, plotting with matplotlib and seaborn, and machine learning with scikit-learn.

Some of the algorithms we will cover in the course include logistic regression, k-nearest neighbors, decision trees, random forests, principal component analysis, k-means, hierarchical clustering, and neural networks. Students will leave the course with a solid understanding of several machine learning algorithms and the ability to use them when appropriate.

While the class will do a review of basic Python, a little programming experience (preferably in Python) is necessary.

Developer Relations, Anyscale

Michael Galarnyk writes about Python on Medium and teaches Python courses through UC San Diego Extension and LinkedIn Learning. He received an MS in data science and engineering from UC San Diego.

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.