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
apply gradient descent to compute the factors of a ratings matrixbuild a baseline model using logistic regressionbuild a logistic regression model using principal componentscompute a penalty for a large number of latent factors when computing the factors of a ratings matrixcompute eigenvalues and eigenvectorscompute the predicted ratings given by users for various items by using matrix decompositiondecompose a ratings matrix into its latent factorsdefine eigenvalues and eigenvectorsdefine eigenvectors and eigenvaluesdefine principal components and their uses
describe the intuition behind collaborative filtering, its main advantages, and how ratings matrices, the nearest neighbor approach, and latent factor analysis are involveddescribe the use cases of recommendation systems and the different techniques applied to build such models, with emphasis on the content-based filtering approachimplement the gradient descent algorithm to decompose a ratings matrixmathematically compute principal componentsperform principal component analysisrecall the intuition behind principal component analysisrecall the use of matrix operations to represent linear transformationssummarize the intuition behind collaborative filtering, its main advantages, and how ratings matrices, the nearest neighbor approach, and latent factor analysis are involvedsummarize the use cases of recommendation systems and the different techniques applied to build such models, with emphasis on the content-based filtering approachuse NumPy and Pandas to define a ratings matrix that can be fed into a recommendation system
IN THIS COURSE
1.Advanced Math33sUP NEXT
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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