fullscreen background
Skip to main content

Winter Quarter

Winter Catalogues
Now Available
Registration Opens Nov 30
shopping cart icon0

Courses

« Back to Professional & Personal Development

CS 08 W — Machine Learning with Python

Quarter: Winter
Course Format: Flex Online (About Formats)
Duration: 10 weeks
Date(s): Jan 11—Mar 19
Drop Deadline: Jan 14
Units: 2
Tuition: $625
Instructor(s): Michael Galarnyk
Limit: 45
Status: Registration opens Nov 30, 8:30 am (PT)
Please Note: Some of our refund deadlines have changed. See this course's drop deadline above and click here for the full policy.
Winter
Flex Online(About Formats)
Date(s)
Jan 11—Mar 19
10 weeks
Drop By
Jan 14
2 Units
Fees
$625
Instructor(s):
Michael Galarnyk
Limit
45
Registration opens Nov 30, 8:30 am (PT)
Please Note: Some of our refund deadlines have changed. See this course's drop deadline above and click here for the full policy.
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.

Michael Galarnyk, Senior Data Scientist, Coding Dojo

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.
DOWNLOAD THE PRELIMINARY SYLLABUS » (subject to change)