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 dаta: 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

Welcome to Bayesian Linear Regression!

I first started this course series on Bayesian Machine Learning many years ago, with a course on A/B Testing. I had always intended to expand the series (there's a lot to cover!) but kept getting pulled in other directions.

Today, I am happy to announce that the Bayesian Machine Learning series is finally back on track!

In the first course, a lot of students asked, "but where is the 'machine learning'?", since they thought of machine learning from the typical supervised/unsupervised parametric model paradigm. The A/B Testing course was never meant to look at such models, but that is exactly what this course is for.

If you've studied machine learning before, then you know that linear regression is the first model everyone learns about. We will approach Bayesian Machine Learning the same way.

Bayesian Linear Regression has many nice properties (easy transition from non-Bayesian Linear Regression, closed-form solutions, etc.). It is best and most efficient "first step" into the world of Bayesian Machine Learning.

Also, let's not forget that Linear Regression (including the Bayesian variety) is simply very practical in the real-world. Bayesian Machine Learning can get very mathematical, so it's easy to lose sight of the big picture - the real-world applications. By exposing yourself to Bayesian ideas slowly, you won't be overwhelmed by the math. You'll always keep the application in mind.

It should be stated however: Bayesian Machine Learning really is very mathematical. If you're looking for a scikit-learn-like experience, Bayesian Machine Learning is definitely too high-level for you. Most of the "work" involves algebraic manipulation. At the same time, if you can tough it out to the end, you will find the results really satisfying, and you will be awed by its elegance.

Sidenote: If you made it through my Linear Regression and A/B Testing courses, then you'll do just fine.

Suggested 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
  
• Bayesian Machine Learning: A/B Testing in Python (know about conjugate priors)
  

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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