Databricks
Introduction to PyMC3 for Bayesian Modeling and Inference
Databricks

Introduction to PyMC3 for Bayesian Modeling and Inference

This course is part of Introduction to Computational Statistics for Data Scientists Specialization

Taught in English

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2,045 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

3.9

(20 reviews)

Beginner level

Recommended experience

11 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • 1. The PyMC3/ArViz framework for Bayesian modeling and inference

    2. Build real-world models using PyMC3 and assess the quality of your models

Details to know

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Assessments

3 quizzes

Course

Gain insight into a topic and learn the fundamentals

3.9

(20 reviews)

Beginner level

Recommended experience

11 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Introduction to Computational Statistics for Data Scientists Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

This module serves as an introduction to the PyMC3 framework for probabilistic programming. It introduces some of the concepts related to modeling and the PyMC3 syntax. The visualization library ArViz, that is integrated into PyMC3, will also be introduced. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

What's included

12 videos3 readings1 quiz

This module will teach the basics of using PyMC3 to solve regression and classification problems using PyMC3. It will also show how to deal with outliers in your data and create hierarchical models. Finally, a case study is presented to help apply everything that was learned in Module 1 and 2. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#linear-regression-again. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

What's included

14 videos1 quiz

This module introduces various measures and metrics to assess the quality of the solutions inferred using PyMC3. Hands-on examples are used to illustrate how various methods and visualizations can be used in PyMC3. Finally, a brief overview of how to debug PyMC3 algorithms is provided. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#mcmc-metrics. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

What's included

11 videos3 readings1 quiz

This is an ungraded final project. We will utilize everything that has been learned in this course to model the disease dynamics of COVID-19 using a SIR model. Utilizing real-life data, the goal would be to infer the parameters of the SIR model for COVID-19.

What's included

1 plugin

Instructor

Instructor ratings
2.8 (5 ratings)
Dr. Srijith Rajamohan
Databricks
3 Courses5,870 learners

Offered by

Databricks

Recommended if you're interested in Machine Learning

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3.9

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