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
Matrix Methods
Instructor: Daniel Boley
Access provided by University of Toronto
13,481 already enrolled
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
Instructor
Instructor ratings
3.8 (54 ratings)Offered by
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4.1
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M
5
Reviewed on Apr 20, 2020
Its a very good experience for me and it helps me to learn new topics and known new matters.Thank You Coursera.
LF
5
Reviewed on Dec 26, 2020
Succinct, informative, efficient. Thank you, Dr. Boley.
AV
5
Reviewed on Aug 31, 2022
This has been a helpful course. I had the chance to learn about practical applications of matrices.
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