K. Naive Bayes

The Naive Bayes course is designed to help learners understand the principles and applications of the Naive Bayes Classifier, one of the widely used methods for data classification in machine learning and data mining. The course content covers the fundamentals of Naive Bayes, starting from the operation of simple models to their implementation in various classification problems, including real-world case studies such as text classification.

Participants will also learn techniques for tuning parameters to enhance model performance. Additionally, the course includes topics on Multinomial Naive Bayes and Bayesian Networks, which expand the capabilities of Naive Bayes to handle more specific data characteristics. Learners will explore techniques for parameter tuning, feature selection, and managing imbalanced data to create high-performing models that are suitable for real-world applications.

This course is ideal for those with a background in machine learning who want to deepen their understanding of classification techniques using Naive Bayes.