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CS 08 W — Machine Learning with Python

Quarter: Summer
Course Format: Flex Online (About Formats)
Duration: 10 weeks
Date(s): Jun 21—Aug 27
Refund Deadline: Jun 24
Units: 2
Tuition: $625
Instructor(s): Michael Galarnyk
Limit: 45
Class Recording Available: Yes
Status: Registration opens May 17, 8:30 am (PT)
DOWNLOAD THE SYLLABUS » (subject to change)
Flex Online(About Formats)
Jun 21—Aug 27
10 weeks
Refund Date
Jun 24
2 Units
Michael Galarnyk
Registration opens May 17, 8:30 am (PT)
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 different 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.

Developer Relations, Anyscale

Michael Galarnyk writes about Python and machine learning on Medium and teaches Python courses through LinkedIn Learning. In the past, he has taught machine learning for UC San Diego Extension. He received an MS in data science and engineering from UC San Diego.