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.
About this Course
Skills you will gain
- 5 stars51.02%
- 4 stars22.60%
- 3 stars12.83%
- 2 stars6.69%
- 1 star6.83%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: PCA
Challenging, but doable. Has some bugs in coding assignments, but clearing them out makes you understand things better. Get ready to spend extra time understanding the concepts.
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.
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).
Great capstone for the three-class Mathematics for Machine Learning series. Assignments were way harder and programming debugging skills had to be appropiate in order to finish the class.
About the Mathematics for Machine Learning Specialization
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