ML & DL Algorithms Competency (Intermediate Level)

  • 20m
  • 10 questions
The ML & DL Algoritms Competency (Intermediate Level) benchmark assesses your recognition of core ML & DL Algoritms You will be evaluated on your skills in recognizing high-level elements of ML & DL Algoritms. Learners who score high on this benchmark demonstrate that they have a solid understanding of intermediate-level ML & DL Algoritms.

Topics covered

  • compare prominent machine learning algorithms and select the appropriate algorithm for diversified problem spaces
  • describe how regression works by finding the best fit straight line to model the relationships in your data
  • distinguish between supervised learning techniques such as regression and classification, and unsupervised learning methods such as clustering
  • escribe how clustering algorithms are able to find data points containing common attributes and thus create logical groupings of data
  • escribe what Support Vector Machines are and how they are used to find a hyperplane to divide data points into categories
  • list the characteristics of regression such as simplicity and versatility, which have led to the widespread adoption of this technique in a number of different fields
  • recognize the application of a confusion matrix and how it can be used to measure the accuracy, precision, and recall of a classification model
  • recognize the different kinds of machine learning algorithms such as regression, classification, and clustering, as well as their specific applications
  • recognize the need to reduce large datasets with many features into a handful of principal components using the PCA technique
  • recognize the problems associated with a model that is overfitted to training data and how to mitigate the issue