In this self-paced course, you will learn how to apply Naive Bayes to many real-world datasets in a wide variety of areas, such as:

• computer vision

• natural language processing

• financial analysis

• healthcare

• genomics

Why should you take this course? Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it.

This 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!

Thank you for reading and I hope to see you soon!

Suggested Prerequisites:

• Decent Python programming skill

• Comfortable with data science libraries like Numpy and Matplotlib

• For the advanced section, probability knowledge is required

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?

• Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including my free course)

UNIQUE FEATURES

• Every line of code explained in detail - email me any time if you disagree

• Less than 24 hour response time on Q&A on average

• Not afraid of university-level math - get important details about algorithms that other courses leave out