Final Exam: Advanced Math

Math    |    Intermediate
  • 1 video | 32s
  • Includes Assessment
  • Earns a Badge
Rating 2.6 of 5 users 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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