Machine Learning Work From Home Part Time Internship

pgmpy (https://github.com/pgmpy/pgmpy) is a widely used open-source Python package for working with Bayesian Networks and Directed Acyclic Graphs. These models are often used in the fields of causal inference and explainable AI. pgmpy provides functionality for building and making inferences from these models. The goal of pgmpy is to make state-of-the-art methods in Causal Inference and Bayesian Networks accessible to researchers and practitioners.

During this internship, you will collaborate closely with the lead developer to design and implement new features for the package. Explore a couple of potential project ideas here: https://github.com/pgmpy/pgmpy/wiki/Mentored-Projects. We also encourage new project proposals that align with your interests and expertise. Feel free to drop us an email to discuss any potential ideas.

What you will learn:

1. Open-source Development Skills: Practical experience contributing to a well-established Python package.
2. Cutting-edge Knowledge: Exposure to and hands-on work with state-of-the-art methods in causal inference.
3. Growth Opportunities: Potential to become a maintainer or collaborate with companies using pgmpy in real-world projects.
4. Mentorship: Direct guidance from the lead developer.

Selected intern's day-to-day responsibilities include:

1. Contribute to developing new features, improving existing ones, and fixing bugs.
2. Collaborate with the lead developer to implement advanced algorithms related to causal inference.
3. Participate in code reviews and discussions to maintain high-quality code standards.
4. Stay updated with the latest research in causal inference and Bayesian networks.

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