Data Science: Bayesian Linear Regression in Python

Fundamentals of Bayesian Machine Learning Parametric Models

Register for this Course

$14.99 $109.99 USD 86% OFF!

Login or signup to register for this course

Have a coupon? Click here.

Course Data

Lectures: 22
Length: 3h 50m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

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)

Testimonials and Success Stories

I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...


I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...


By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.


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

7 Lectures · 01hr 22min
  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)
  7. Suggestion Box (03:10)

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
This website is using cookies. That's Fine