Data Science: Bayesian Linear Regression in Python

Fundamentals of Bayesian Machine Learning Parametric Models

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Course Data

Lectures: 21
Length: 3h 47m
Skill Level: All Levels
Languages: English
Includes: Lifetime access

Course Description

Welcome to Bayesian Linear Regression!

I first started this course series on Bayesian Machine Learning 5 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)


Introduction and Outline

4 Lectures · 19min
  1. Introduction (06:30) (FREE preview available)
  2. Outline (03:26)
  3. Where to get the code (00:29)
  4. The Big Picture (Optional) (09:05)

Review of Classical Linear Regression

6 Lectures · 01hr 19min
  1. Simple Linear Regression Review (05:24)
  2. Distribution of w Estimate (08:14)
  3. Linear Regression Review Dog Food (20:58)
  4. Relationship to Maximum Likelihood Estimation (04:15)
  5. MAP Estimation (10:09)
  6. MLE and MAP Dog Food (30:45)

Bayesian Linear Regression With One Input

8 Lectures · 01hr 39min
  1. The Bayesian Approach (08:05)
  2. Review of Conjugate Priors (05:38)
  3. Training: Posterior w (06:51)
  4. Making Predictions (pt 1) (05:32)
  5. Making Predictions (pt 2) (04:42)
  6. Making Predictions (pt 3) (06:16)
  7. Training Dog Food (21:06)
  8. Prediction Dog Food (41:04)

Bayesian Linear Regression With Multiple Inputs

2 Lectures · 13min
  1. Multivariate Bayesian Linear Regression (Fitting) (08:15)
  2. Multivariate Bayesian Linear Regression (Predictions) (05:18)

Bayesian Linear Regression in Code

1 Lectures · 14min
  1. Code (14:50)


  • Bayesian Linear Regression Code
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