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Machine & Deep Learning Algorithms: Regression & Clustering

Machine & Deep Learning Algorithms: Regression & Clustering


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore the fundamentals of regression and clustering and discover how to use a confusion matrix to evaluate classification models.



Expected Duration (hours)
0.8

Lesson Objectives

Machine & Deep Learning Algorithms: Regression & Clustering

  • recognize the application of a confusion matrix and how it can be used to measure the accuracy, precision, and recall of a classification model
  • describe how regression works by finding the best fit straight line to model the relationships in your data
  • 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
  • distinguish between supervised learning techniques such as regression and classification, and unsupervised learning methods such as clustering
  • describe how clustering algorithms are able to find data points containing common attributes and thus create logical groupings of data
  • recognize the need to reduce large datasets with many features into a handful of principal components using the PCA technique
  • to recall concepts such as precision and recall and the use cases for unsupervised learning
  • Course Number:
    it_dsmdladj_02_enus

    Expertise Level
    Beginner