Machine Learning with Naïve Bayes

Introducing you to the topics of Bayesian statistics and Naïve Bayes algorithms in Python’s scikit-learn library.

with Hristina Hristova

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Course Overview

Knowledge on various machine learning algorithms is essential for machine learning enthusiasts and specialists. This course focuses on a specific type of classifier – the Naïve Bayes one. It is famous for being a quick learner and a real-time problem solver. Not only will you learn the theoretical foundations behind the Bayesian approach, but you will also get the chance to solve a real-life problem using scikit-learn’s Naïve Bayes algorithms.

14 High Quality Lessons
6 Practical Tasks
1 Hours of Video
Certificate of Achievement

Skills you will gain

machine learningmathematicsprogrammingpythontheory

What You'll Learn

Aiming to expand your machine learning toolbox? Here is how this course will help you!

Understand the components of Bayes’ theorem
Apply Bayes’ theorem
What “naïve” is in Naïve Bayes
Use scikit-learn’s Multinomial Naïve Bayes
Pros and cons of the Naïve Bayes algorithm
Applications of the Naïve Bayes algorithm


“Learning various machine learning techniques expands your horizons in the field and teaches you how to think outside of the box. It makes you a skilled programmer and a better problem-solver. This course introduces you to a rather simple, yet quite powerful algorithm. Sophisticated algorithms would always serve you well, but sometimes simpler is better!”

Hristina Hristova
Content Creator at 365 Data Science
Machine Learning with Naïve Bayes

with Hristina Hristova

Start Course