Loading...

Course Description

In this course, you will be introduced to the classification and regression trees (CART) algorithm. By implementing CART, you will build decision trees for a supervised classification problem. Next, you will explore how the hyperparameters of an algorithm can be adjusted and what impact they have on the accuracy of a predictive model. Through this exploration, you will practice selecting an appropriate model for a problem and dataset. You will then load a live dataset, select a model, and train a classifier to make predictions on that data.

The following courses are required to be completed before taking this course:

  • Problem-Solving with Machine Learning
  • Estimating Probability Distributions
  • Learning with Linear Classifiers

Faculty Author

Kilian Weinberger

Benefits to the Learner

  • Implement the CART splitting algorithm to find the best split for a given data set and impurity function
  • Implement the CART algorithm to build classification and regression trees
  • Choose the appropriate model that performs best for your problem and data using grid search and cross validation
  • Implement a machine learning setup from start to finish
  • Train and tune your classifier to maximize test accuracy

Target Audience

  • Programmers
  • Developers
  • Data analysts
  • Statisticians
  • Data scientists
  • Software engineers

Applies Towards the Following Certificates

Loading...
Cornell Computing and Information Science
Thank you for your interest in this course. Unfortunately, the course you have selected is currently not open for enrollment. Please complete a Course Inquiry so that we may promptly notify you when enrollment opens.
Required fields are indicated by .