Popular machine learning algorithms: Naive Bayes and PCA
Welcome to the learning path for popular machine learning algorithms: Naive Bayes and PCA! The content in this learning path pairs with in-person workshops that run in Microsoft Reactor and are standalone learning resources. (You don't have to come to a workshop to benefit from these modules.) Throughout this learning path, you'll be encouraged to test Python code in Visual Studio Code by using the Python extension and Jupyter Notebooks.
In this learning path, you'll:
- Learn the strengths and limitations of conditional probability and Naive Bayes machine learning.
- Learn how to use principal component analysis to learn about the contents of a food nutrition dataset.
This is complementary content for Microsoft Reactor Workshops.
Prerequisites
- Introduction to Python for data science
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Modules in this learning path
Learn how to use conditional probability and Bayes classification to analyze an actual dataset of email messages. You'll learn how to use and apply these machine learning principles as you analyze an email dataset for spam and ham.
Learn how to use principal component analysis (PCA) to learn about the contents of a food nutrition dataset.