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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Mathematik für maschinelles Lernen
von
Über diesen Kurs
Was Sie lernen werden
Implement mathematical concepts using real-world data
Derive PCA from a projection perspective
Understand how orthogonal projections work
Master PCA
Kompetenzen, die Sie erwerben
- Dimensionality Reduction
- Python Programming
- Linear Algebra
Lehrplan - Was Sie in diesem Kurs lernen werden
Statistics of Datasets
Inner Products
Orthogonal Projections
Principal Component Analysis
Bewertungen
- 5 stars51,12 %
- 4 stars22,58 %
- 3 stars12,74 %
- 2 stars6,65 %
- 1 star6,89 %
Top-Bewertungen von 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!
Very challenging at times, but very good course none the less. Would recommend to any one who has a solid foundation of Linear Algebra (Course 1) and Multivariate Calculus (Course 2).
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.
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.
Über den Spezialisierung Mathematik für maschinelles Lernen

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