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Course Description

Once you have trained your model, how do you know whether it will generalize well to new data? In this course, you will focus on techniques that can be used to properly evaluate and improve a model's performance with the view toward producing the best model for your data and machine learning problem. You will explore different model selection methods that are used to find the best-performing model, and you will apply common out-of-sample validation methods that are used to test your model on unseen data in support of model selection.

You will also discover how both hyperparameter configurations as well as feature combinations play roles in model performance. Using your own implementation along with built-in scikit-learn libraries, you will determine the optimal hyperparameter configuration for your model and perform feature selection techniques to find the combination of features that results in the best model performance.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Machine Learning Foundations
  • Managing Data in Machine Learning
  • Training Common Machine Learning Models
  • Training Linear Models

Faculty Author

Brian D'Alessandro

Benefits to the Learner

  • Understand the importance of model selection in machine learning
  • Choose model evaluation metrics that are appropriate for the application
  • Choose appropriate model candidates and hyperparameters for testing
  • Set up training, validation, and test splits for model selection
  • Apply feature selection techniques to get a better-performing model

Target Audience

  • Data scientists and data analysts
  • Programmers, developers, and software engineers
  • Statisticians
  • Product managers
  • Entrepreneurs
  • Working professionals seeking to upskill or career change

Applies Towards the Following Certificates

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Enroll Now - Select a section to enroll in
Type
2 week
Dates
Jun 05, 2024 to Jun 18, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Aug 28, 2024 to Sep 10, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Nov 20, 2024 to Dec 03, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
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