Udemy – Data Science: Bayesian Linear Regression in Python 2025-5
Udemy – Data Science: Bayesian Linear Regression in Python 2025-5 Downloadly IRSpace
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Data Science: Bayesian Linear Regression in Python is a course on Bayesian Methods for Regression Modeling published by Udemy Online Academy. It is a specialized course designed to introduce data scientists, analysts, and machine learning professionals to Bayesian methods for regression modeling. Focusing on the powerful combination of probability theory and statistical inference, this course takes an in-depth look at Bayesian linear regression, comparing it to classical approaches, and showing how to incorporate prior beliefs, update them with observed data, and interpret posterior distributions.
If you are looking for an experience similar to PsycheLearn, Bayesian machine learning is definitely too high-level for you. Most of the “work” involves algebraic manipulation. At the same time, if you can stick with it until the end, you will find the results truly satisfying and will be amazed by its elegance. This course is recommended for data scientists and machine learning professionals who want to learn Bayesian linear regression from theory to coding, and for students and professionals who are curious about Bayesian methods and their real-world applications.
What you will learn in Data Science: Bayesian Linear Regression in Python:
- Understand Bayesian Linear Regression: Learn how Bayesian inference is applied to linear regression using priors and posteriors
- Model Extraction and Implementation: Do the math and code for Bayesian linear regression in Python from scratch
- Compare Bayesian vs. Frequentist Methods: Explore the key differences and advantages of Bayesian over traditional linear regression
- Apply Bayesian Regression to Data: Use probabilistic modeling to analyze real-world datasets and quantify uncertainty
- And…
Course specifications
Publisher: Udemy
Instructors: Lazy Programmer Inc. and Lazy Programmer Team
Language: English
Level: Advanced
Number of Lessons: 30
Duration: 4 hours and 47 minutes
Course topics
Data Science: Bayesian Linear Regression in Python Prerequisites
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
Pictures
Data Science: Bayesian Linear Regression in Python introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: English
Quality: 720p
Changes:
Version 2025/5 compared to 2025/3 has increased by 3 lessons and 48 minutes in duration. English subtitles were also added to the course.
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Size
1.86 GB
