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 stars51.01%
- 4 stars22.60%
- 3 stars12.84%
- 2 stars6.69%
- 1 star6.83%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: PCA
This course was definitely a bit more complex, not so much in assignments but in the core concepts handled, than the others in the specialisation. Overall, it was fun to do this course!
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).
The course is generally good but the assignment setting definitely needs to be rectified. Thanks anyway for this course. An important element of machine learning.
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.
About the Mathematics for Machine Learning Specialization
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