Data Science & Machine Learning: Naive Bayes in Python
Data Science & Machine Learning: Naive Bayes in Python
Master a crucial artificial intelligence algorithm and skyrocket your Python programming skills
Created by Lazy Programmers Inc., Lazy Programmer Team | 7.5 hours of video course
This Data Science & Machine Learning: Naive Bayes in Python course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You’ll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm. You’ll learn about when and why to use the different versions of Naive Bayes included in Scikit-Learn, including GaussianNB, BernoulliNB, and MultinomialNB.
In the advanced section of the course, you will learn about how Naive Bayes really works under the hood. You will also learn how to implement several variants of Naive Bayes from scratch, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes. The advanced section will require knowledge of probability, so be prepared!
What you’ll learn
- Apply Naive Bayes to image classification (Computer Vision)
- Apply Naive Bayes to text classification (NLP)
- Apply Naive Bayes to Disease Prediction, Genomics, and Financial Analysis
- Understand Naive Bayes concepts and algorithm
- Implement multiple Naive Bayes models from scratch
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