Final Exam: Advanced Math
Math | Intermediate
- 1 video | 32s
- Includes Assessment
- Earns a Badge
Final Exam: Advanced Math will test your knowledge and application of the topics presented throughout the Advanced Math track of the Skillsoft Aspire Essential Math for Data Science Journey.
WHAT YOU WILL LEARN
recall the use of matrix operations to represent linear transformationsdefine eigenvectors and eigenvaluesdefine principal components and their usesrecall the intuition behind principal component analysisdefine eigenvalues and eigenvectorsmathematically compute principal componentscompute eigenvalues and eigenvectorsperform principal component analysisbuild a baseline model using logistic regressionbuild a logistic regression model using principal components
summarize the use cases of recommendation systems and the different techniques applied to build such models, with emphasis on the content-based filtering approachdescribe the use cases of recommendation systems and the different techniques applied to build such models, with emphasis on the content-based filtering approachsummarize the intuition behind collaborative filtering, its main advantages, and how ratings matrices, the nearest neighbor approach, and latent factor analysis are involveddescribe the intuition behind collaborative filtering, its main advantages, and how ratings matrices, the nearest neighbor approach, and latent factor analysis are involveddecompose a ratings matrix into its latent factorsapply gradient descent to compute the factors of a ratings matrixcompute a penalty for a large number of latent factors when computing the factors of a ratings matrixuse NumPy and Pandas to define a ratings matrix that can be fed into a recommendation systemimplement the gradient descent algorithm to decompose a ratings matrixcompute the predicted ratings given by users for various items by using matrix decomposition
IN THIS COURSE
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