Math    |    Intermediate
• 1 video | 32s
• Includes Assessment
Rating 2.6 of 5 users (5)
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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