Chevron Left
Back to Introduction to PyMC3 for Bayesian Modeling and Inference

Learner Reviews & Feedback for Introduction to PyMC3 for Bayesian Modeling and Inference by Databricks

3.9
stars
20 ratings

About the Course

The objective of this course is to introduce PyMC3 for Bayesian Modeling and Inference, The attendees will start off by learning the the basics of PyMC3 and learn how to perform scalable inference for a variety of problems. This will be the final course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3.. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html. The instructor for this course will be Dr. Srijith Rajamohan....

Top reviews

Filter by:

1 - 9 of 9 Reviews for Introduction to PyMC3 for Bayesian Modeling and Inference

By Wang X

Jan 5, 2022

Can not reproduce the results from the codes, and no helps gotten either from the forums nor reply from instructors (through email).

By Wojciech W

Jun 2, 2022

The course is taught in the most awful way - the teacher is reading notes that are displayed on the screen. But the materials provided, which are the notes themselves and tones of Python-Jupyter code are mostly great. I ended up reading the notes and scrolling through the (very short) videos, played at 1.5 speed. I've learned what I was hoping to learn.

By Lawrence A J

Apr 21, 2023

Good introduction to the topic. Thanks!

Would like more.

By Flavio L

Jan 24, 2022

amazing, nice material, well explained

By Shahid R

May 24, 2023

Great Course

By Gerald M

Sep 14, 2022

Great capstone providing useful practice and tools for applying the concepts.

By Ethan H

Feb 12, 2024

The course materials are actually alright, but the lecture doesn't serve what I consider to be its core function which is to provide intuition and summarize complexity with simplicity. These lectures don't even contribute another angle to the material, they are just read verbatim from the course materials so the experience is more like text-to-speech than another human trying to communicate new and challenging ideas to you. I'd only take this course if you are comfortable with full self study on a subject

By Jack R

May 7, 2022

Will teach you a lot about Bayesian statistics, but often times the code is not well suited for beginners. Especially the final project. They give the code to do it, but a standard logistic regression problem would have been better suited rater than a differential equations model with various classes.

By Rafael B R

Aug 10, 2021

Coding evaluations don't exist as promised, the more advanced content regarding MCMC diagnosis and applicate bayesian inference is just thrown away without good explanations