Data Science & Machine Learning Naive Bayes in Python

Data Science & Machine Learning Naive Bayes in Python
Published 11/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch

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

Requirements
Decent Python programming skills
Experience with Numpy, Matplotlib, and Pandas (we’ll be using these)
For advanced portions: know probability

Description
computer vision

natural language processing

financial analysis

healthcare

genomics

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.

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

Who this course is for
Beginner Python developers curious about data science and machine learning
Students and professionals interested in machine learning fundamentals