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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

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Enroll Now - Select a section to enroll in
Type
2 week
Dates
Nov 05, 2025 to Nov 18, 2025
Total Number of Hours
18.0
Course Fee(s)
Regular Price $1,199.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note the content in the Machine Learning course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order. For example; CIS531 prior to CIS532, CIS532 prior to CIS533, etc.

  • This program also includes two additional self-paced linear algebra courses to assist you in your coursework. You will be automatically enrolled in the courses and can access them at any time before or during your Machine Learning program. If you need to refresh your linear algebra skills, it is highly recommended that you access these additional resources prior to the start of your first course.

Type
2 week
Dates
Dec 03, 2025 to Dec 16, 2025
Total Number of Hours
18.0
Course Fee(s)
Regular Price $1,199.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note the content in the Machine Learning course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order. For example; CIS531 prior to CIS532, CIS532 prior to CIS533, etc.

  • This program also includes two additional self-paced linear algebra courses to assist you in your coursework. You will be automatically enrolled in the courses and can access them at any time before or during your Machine Learning program. If you need to refresh your linear algebra skills, it is highly recommended that you access these additional resources prior to the start of your first course.

Type
2 week
Dates
Dec 31, 2025 to Jan 13, 2026
Total Number of Hours
18.0
Course Fee(s)
Regular Price $1,199.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note the content in the Machine Learning course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order. For example; CIS531 prior to CIS532, CIS532 prior to CIS533, etc.

  • This program also includes two additional self-paced linear algebra courses to assist you in your coursework. You will be automatically enrolled in the courses and can access them at any time before or during your Machine Learning program. If you need to refresh your linear algebra skills, it is highly recommended that you access these additional resources prior to the start of your first course.

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