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:

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)

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.

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.

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

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. :)

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. ...

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- Introduction (06:30) (FREE preview available)
- Outline (03:26)
- Where to get the code (00:29)
- The Big Picture (Optional) (09:05)

- Simple Linear Regression Review (05:24)
- Distribution of w Estimate (08:14)
- Linear Regression Review Dog Food (20:58)
- Relationship to Maximum Likelihood Estimation (04:15)
- MAP Estimation (10:09)
- MLE and MAP Dog Food (30:45)
- Suggestion Box (03:10)

- The Bayesian Approach (08:05)
- Review of Conjugate Priors (05:38)
- Training: Posterior w (06:51)
- Making Predictions (pt 1) (05:32)
- Making Predictions (pt 2) (04:42)
- Making Predictions (pt 3) (06:16)
- Training Dog Food (21:06)
- Prediction Dog Food (41:04)

- Multivariate Bayesian Linear Regression (Fitting) (08:15)
- Multivariate Bayesian Linear Regression (Predictions) (05:18)

- Code (14:50)

- Bayesian Linear Regression Code