Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction. Optional examples using Python are used to illustrate the concepts and allow the learner to experiment with the algorithms.
Matrix Methods
Gain insight into a topic and learn the fundamentals.
4.1
(248 reviews)
Intermediate level
Some related experience required
6 hours to complete
Flexible schedule
Learn at your own pace
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Assessments
18 assignments
Taught in English
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There are 5 modules in this course
What's included
3 videos2 readings3 assignments
What's included
3 videos2 readings3 assignments
What's included
4 videos3 readings4 assignments
What's included
4 videos2 readings5 assignments
What's included
2 readings3 assignments
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3.8 (54 ratings)
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4.1
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Showing 3 of 248
TT
5
Reviewed on May 17, 2020
This Course content is very good and has good real-time examples. However, the Instructor should add a few videos on SVD, Maximum dilation, and Shrinkage and Direction of Maximum Dilation.
SJ
5
Reviewed on Mar 16, 2020
Really its very informative and basic material provided. I enjoyed and learn a lot
LF
5
Reviewed on Dec 26, 2020
Succinct, informative, efficient. Thank you, Dr. Boley.
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