https://github.com/brianspiering/naive_bayes_intro

Introduction to Naive Bayes - a Machine Learning Classification Algorithm
https://github.com/brianspiering/naive_bayes_intro

artificial-intelligence data-science machine-learning

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Introduction to Naive Bayes - a Machine Learning Classification Algorithm

• Host: GitHub
• URL: https://github.com/brianspiering/naive_bayes_intro
• Owner: brianspiering
• License: apache-2.0
• Created: 2022-07-22T18:45:26.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-23T15:25:47.000Z (about 2 years ago)
• Last Synced: 2023-08-01T03:24:23.251Z (over 1 year ago)
• Topics: artificial-intelligence, data-science, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 2.67 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: license

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README

        
Naive Bayes Introduction

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![](images/dare_to_be_naive.jpeg)

Naive Bayes is a fundamental machine learning algorithm. Despite its simplicity, Naive Bayes is one of the useful and commonly used classification algorithms. This talk will cover how the algorithm works and implement the Naive Bayes algorithm from scratch. Common use cases and practical examples will be shown. This talk is designed for beginners who have an interest in machine learning, prior exposure to probability and Python would be helpful to get the most out of it. By the end, you should have a deeper understanding of Naive Bayes.

Slides and code are [here](https://github.com/brianspiering/naive_bayes_intro/blob/main/naive_bayes_intro.ipynb).

About Me

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Brian Spiering is Data Science Instructor at Metis. He teaches humans the languages of computers (primarily Python) and teaches computers the languages of humans (through Natural Language Processing and Artificial Intelligence). He is active in global tech community through volunteering and mentoring.