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 4 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:
- Build models that get adopted by an organisation or customers
- 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!