Data Science: Bayesian Linear Regression in Python

Data Science: Bayesian Linear Regression in Python
2025-12-02
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Fundamentals of Bayesian Machine Learning Parametric Models

What you’ll learn
Understand Bayesian Linear Regression: Learn how Bayesian inference applies to linear regression using priors and posteriors.
Derive and Implement the Model: Work through the math and code Bayesian Linear Regression from scratch in Python.
Compare Bayesian vs. Frequentist Methods: Explore key differences and benefits of Bayesian over traditional linear regression.
Apply Bayesian Regression to Data: Use probabilistic modeling to analyze real-world datasets and quantify uncertainty.

Requirements
Python coding: if/else, loops, lists, dicts, sets
Numpy and Pandas coding: matrix and vector operations, loading a CSV file
Basic math: calculus, linear algebra, probability
Linear regression
A bit of Bayesian statistics: just know about conjugate priors

Description
Who this course is for:
Data scientists and ML practitioners who want to master Bayesian Linear Regression from theory to code., Students and professionals curious about Bayesian methods and their real-world applications., ML enthusiasts who love understanding models mathematically and implementing them from scratch., Anyone with basic Python and probability skills looking to apply Bayesian regression in data science., Anyone who wants to go beyond Scikit-Learn and truly understand Bayesian Machine Learning.

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