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 stars51.09%
- 4 stars22.58%
- 3 stars12.76%
- 2 stars6.70%
- 1 star6.84%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: PCA
Definitely the most challenging of the course making up this specialization. Finishing it with full scores is proportionally far more satisfying!!! Well done Marc!
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.
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).
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.
About the Mathematics for Machine Learning Specialization
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