Math    |    Intermediate
• 1 video | 32s
• Includes Assessment
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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 transformations define eigenvectors and eigenvalues define principal components and their uses recall the intuition behind principal component analysis define eigenvalues and eigenvectors mathematically compute principal components compute eigenvalues and eigenvectors perform principal component analysis build a baseline model using logistic regression build 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 approach describe the use cases of recommendation systems and the different techniques applied to build such models, with emphasis on the content-based filtering approach summarize the intuition behind collaborative filtering, its main advantages, and how ratings matrices, the nearest neighbor approach, and latent factor analysis are involved describe the intuition behind collaborative filtering, its main advantages, and how ratings matrices, the nearest neighbor approach, and latent factor analysis are involved decompose a ratings matrix into its latent factors apply gradient descent to compute the factors of a ratings matrix compute a penalty for a large number of latent factors when computing the factors of a ratings matrix use numpy and pandas to define a ratings matrix that can be fed into a recommendation system implement the gradient descent algorithm to decompose a ratings matrix compute the predicted ratings given by users for various items by using matrix decomposition

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