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
Offered By
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
Master PCA
Skills you will gain
- Dimensionality Reduction
- Python Programming
- Linear Algebra
Offered by
Syllabus - What you will learn from this course
Statistics of Datasets
Inner Products
Orthogonal Projections
Principal Component Analysis
Reviews
- 5 stars51.31%
- 4 stars22.58%
- 3 stars12.72%
- 2 stars6.61%
- 1 star6.75%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: PCA
Programming assignment for week 1 wastes to much time due to lack of instructions.
The notebook also does not work...(maybe locally , but I have other things to do).
Well explained, some issues with assignments but some of them are to not just type and think a little.
May be one is a real mistake... hard time with it, but lot of learning too.
Overall the course was great. The only thing was that there was a lot I didn't understand from the videos. The recommended textbook resource was a great help.
Course content is interesting and well planned, Can be improved by making it Simpler for Students as it was more technical than the other 2 courses of the Specialization.
About the Mathematics for Machine Learning Specialization

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