This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.
This course is part of the Mathematics for Machine Learning Specialization
About this Course
What you will learn
Implement mathematical concepts using real-world data
Derive PCA from a projection perspective
Understand how orthogonal projections work
Skills you will gain
- Dimensionality Reduction
- Python Programming
- Linear Algebra
Syllabus - What you will learn from this course
Statistics of Datasets
Principal Component Analysis
- 5 stars51.11%
- 4 stars22.57%
- 3 stars12.76%
- 2 stars6.70%
- 1 star6.84%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: PCA
It is a bit difficult and jumpy. You will need some hard work to fill in the missing links of knowledge which not explicite on the lectrue. Overall, great experience.
Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.
Very challenging course, requires intermediate knowledge of Python and the numpy library. PCA week 4 lab was truly a mind-blowing experience, taking over 5 hours to complete.
This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.
About the Mathematics for Machine Learning Specialization
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