pymc-learn: Practical Probabilistic Machine Learning in Python


Currently, there is a growing need for principled machine learning approaches by non-specialists in many fields including the pure sciences (e.g. biology, physics, chemistry), the applied sciences (e.g. political science, biostatistics), engineering (e.g. transportation, mechanical), medicine (e.g. medical imaging), the arts (e.g visual art), and software industries. This has lead to increased adoption of probabilistic modeling. However, usage of PPLs requires a specialized understanding of probability theory, probabilistic graphical modeling, and probabilistic inference. Some PPLs also require a good command of software coding. These requirements make it difficult for non-specialists to adopt and apply probabilistic machine learning to their domain problems. Pymc-learn seeks to address these challenges by providing state-of-the art implementations of several popular probabilistic machine learning models. It is inspired by scikit-learn and focuses on bringing probabilistic machine learning to non-specialists. It puts emphasis on ease of use, productivity, fexibility, and performance.

McLean, Virginia


Only a laptop with a modern web browser like Google chrome or Mozilla Firefox.