# Machine Learning with Naïve Bayes

4.8/5
(601)

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

2 hours 14 lessons
14 High Quality Lessons
2 Hours of Content
Certificate of Achievement

## 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.

### Topics covered

machine learningMathematicsProgrammingPythonTheory

### What You'll Learn

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

### Curriculum

Student feedback

4.8/5

601 ratings
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• Oldest
09.11.2022
Very well explained, rate it a 10 out of 5. But I would have been happy if this tutorial would have been thought in google colab, as some of us have troubles installing Jupyter notebook, and now we cant code along. Set back, but however the teaching is great
07.05.2023
Amazing course with great explanations and a short informative course, even better is if you have doubts then most probably you get them answered by an instructor within a day or two
18.06.2023
Things are getting more clearer when getting into deep .Nice course on Naive Bayes to understand how this theorm works on the real time case .Thanks for Hristiana (course creator)
18.11.2022
Totally new to the course. Can consider myself as a beginner & finds it difficult to understand. But its a high level course with a well planned way of presentation. Hats off.
24.01.2023
The contents of the course has enable me to kick-off my Sustainability Project Management. It was a great learning and applying the knowledge into the data science project.
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“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

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