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

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