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Course Description

One of the most important steps in the machine learning process is understanding and preparing data. Before you can learn to train models, you need to ensure the data selected for your model is appropriate to solve the problem.

In this course, you will focus on taking raw data, analyzing and organizing it, and preparing it for the next stage of the machine learning process: modeling. You will practice identifying examples, along with their features and labels, to prepare for supervised learning. You will also practice organizing your data into a data matrix. You will learn about feature engineering, which will allow you to transform your data into a format that is most appropriate for your specific model. By the end of the course, you will be set up with the necessary foundations for managing data in ML.

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

  • Machine Learning Foundations

Faculty Author

Brian D'Alessandro

Benefits to the Learner

  • Build a data set suitable for ML applications
  • Create an appropriate label for supervised learning
  • Create features that are suitable for ML applications
  • Use exploratory analysis to understand your data
  • Identify and fix issues with your data

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
Jul 17, 2024 to Jul 30, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Oct 09, 2024 to Oct 22, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
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