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Course Description

After data has been prepared, the next step in the machine learning lifecycle is model training and evaluation. In this course, you will focus on the model training and evaluation process for supervised learning models and explore a few supervised learning algorithms that are commonly used. You will be introduced to the model training for two popular supervised learning algorithms: k-nearest neighbors (KNN) and decision trees (DT), exploring their applicability to classification problems. You will practice creating your own machine learning models using a popular Python package for machine learning called scikit-learn. By the end of this course, you will have new, applicable skills in training common ML models.

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

Faculty Author

Brian D'Alessandro

Benefits to the Learner

  • Define the core foundational elements of model training and evaluation
  • Develop intuition for different classes of algorithms
  • Analyze the mechanics of two popular supervised learning algorithms: decision trees and k-nearest neighbors
  • Develop intuition on tradeoffs between different algorithmic choices

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
May 08, 2024 to May 21, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Jul 31, 2024 to Aug 13, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Oct 23, 2024 to Nov 05, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
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