Make your models explainable and impact the bottom line

Building easy to interpret models isn’t a nice to have anymore it is the reason people pay for models in the first place

Level up your Analytical/Data Science skills

Check out this free screencast below.

My complete, self-study probabilistic programming and Bayesian Machine Learning course is trusted by members of top machine learning schools, companies, and organizations, including Harvard, Quantopian, Farfetch, Intercom, OKCupid, DoorDash, Mailchimp, Uber, Google, University of Chicago and more! 

Bayesian methods are powerful tools for data science applications, complimenting traditional statistical and machine learning methods. Importantly, Bayesian models generate predictions and inferences that fully account for uncertainty. The main tool for conducting Bayesian analysis is Markov chain Monte Carlo (MCMC), a computationally-intensive numerical approach that allows a wide variety of models to be estimated. MCMC algorithms are available in several Python libraries, including PyMC3. I teach users a practical, effective workflow for applying Bayesian statistics using MCMC via PyMC3 (and other libraries) using real-world examples.

If you can use basic python and build a simple statistical or ML model - this course is for you.

Alumni testimonials

Peadar has turned his practical experience with Bayesian methods into a course that explains the nuts and bolts of Bayesian statistics and probabilistic programming at a good pace.#PyMC3 #ArviZ
Osvaldo Martin - PyMC3 and ArviZ contributor
From the outside, information about this topic is not easy to find. If you do find something, it is usually more advanced material or you need a lot of time to get through it. ... the course saved me time by slowly exposing me to problems and examples that I am able to go through in my own tempo. Having someone to ask when I get stuck is also valuable.
Marko P. - Data Scientist
Peadar has been producing insightful educational material on Data Science and Bayesian Stats for years. Increasingly these Bayesian methods will become important, particularly in regulated sectors.
Alejandro Correra Bahnsen - VP Research
I'm currently doing your probabilistic programming primer, after recently completing a data science immersive course - and I think it's brilliant. Very easy to understand, thanks!
Justin Crowe - Data Scientist
Peadar has a great deal of experience working with probabilistic programming and communicates the fundamentals of Bayesian methods extremely well. He is in an excellent position to guide people through a course like this.
Eoin Hurrell - Data Scientist
I was so impressed with the clarity of Peadars' vision and writing that I included references from him in an open access online course, Sport Informatics and Analytics.
Professor Keith Lyons
Unlike academia or blogs which focus solely on theory or application,  Peadar combines both in those course to set a solid foundation for his students. With the knowledge from this course students will be empowered in Bayesian methods, whether they want to read papers, or start applying the methods in PyMC3 themselves
Ravin Kumar - Engineer and Course student
The probabilistic programming primer is an incredible course that offers a fast track to an incredibly exciting field. Peadar clearly communicates the content and combines this with practical examples which makes it very accessible for his students to get started with probabilistic programming. 
Peter Verheijen - Entrepreneur and Course Student
There's an awesome course on Bayesian Stats by one of the core PyMC3 developers - you should check it out
Hubert Wassner - Chief Data Scientist

PPP | Probabilistic Programming Primer | Bayesian Stats | PyMC3

Probabilistic Programming is one of those tricky areas of Machine Learning, this in depth course will be your guide. 
View course $375

Intro to Probabilistic Programming

The barebones introduction to PyMC3. Contains just enough to get started. 
View course $60