Build better, interpretable models and incorporate domain knowledge

Learn how to enhance your modelling abilities and better communicate risk. 

Overview

Level up your Data Science skills with Probabilistic Programming

Incorporate domain knowledge, handle small but complex data and enhance your interpretability of your models.

Bayesian models are used in a range of verticals such as Pharmaceuticals, Travel, Insurance and Finance.

This course provides over 3 and half hours of exclusive content.

Building interpretable models isn’t nice to have anymore it’s table stakes

Most analysts and data scientists are obsessed with Machine Learning. However XGBoost doesn’t perform well on all problems - particularly small data problems or problems where you need interpretability. 

In an era of more regulation, such as GDPR, it won’t be enough for us as Data Scientists to just say ‘the blackbox says this’ we’ll need to produce interpretable models. 

You are paid as an analyst or Data Scientist or Engineer to support decision making. Whether it is building production systems or not.

In other words, you get paid to:

  1. Build models that get adopted by an organisation or customers
  2. Provide highly insightful (and influential) advice on a business strategy or process

This is impossible without trust. If consumers don't trust your models, or stakeholders don't. Your models won't get adopted and you won't be able to have the impact that Data Scientists need to have. 

If you don’t get buy-in or trust for your models, you're leaving money on the table.

If you’re sick of not having the impact you expected as a Data Scientist, or just want to keep up to date with the next-big-thing then you've come to the right place. 


Step-by-step Instructions for Building more Interpretable models and opening up new product building opportunities


We built Probabilistic Programming Primer to give engineers and data scientists a step-by-step guide to learning how to incorporate domain expertise and build more interpretable models. Last year at the celebrated AI conference NIPS - there were workshops on interpretability and trust is the number one concern that most people have about AI techniques. 

Our mission is to give data scientists and engineers the training and tools that they need to stop worrying about things like not getting buy in, how to incorporate domain knowledge into models, and how to deal with complex small data problems.

As an Probabilistic Programming Primer student, you will receive:

  • Introductions to Bayesian Statistics, PyMC3, Theano and MCMC
  • Descriptive Overviews of Core Models and the Value of Probabilistic Programming
  • Walkthrough Videos That Show You Exactly How to Build and Debug these models
  • Documents and Notebooks Designed to Help you upskill and understand the technical underpinnings and how Probabilistic Programming relates to Deep Learning. These are based on hours of lectures and workshops internally at top startups and at major Data Science conferences.
  • Guidance and Support From a Large (and Growing!) Community of Like-minded Data Scientists
  • Lifetime Access to Our Private Slack Community of Over 150 Ambitious Data Scientists and some core contributors of PyMC3 ($130/year value) 
  • Over 20 screencasts - Screencasts taking you through both the theory and the implementation of Probabilistic Programming.

There is no doubt in our minds that the investment you make in this course will pay for itself 10 times over as long as you implement the techniques we share with you and use the time you save to serve more clients or get more buy-in from your colleagues. It also opens up an entire new niche of skills that would be difficult

Ready to level up your data science skills?

Let's get started!



Register Today

Level up your Analytical/Data Science skills

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

What the pros and students are saying

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 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
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
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

Probabilistic Programming Primer

Probabilistic Programming is one of those tricky areas of Machine Learning and Applied Statistics. In this course join Peadar Coyle a core-developer of PyMC3 as...

View course £150

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