Monthly Subscription Starting at AED 99 + VAT

Introduction to PyMC3 for Bayesian Modeling and Inference
  • Level Foundation
  • Duration 12 hours
  • Course by Databricks
  • Offered by

About

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.

Modules

Introduction to PyMC3

    • 7 Videos
    • 3 Readings
Show info about module content

7 Videos

  • Welcome to Course 3!
  • Probabilistic Programming with PyMC3
  • An introduction to PyMC3
  • Inference with PyMC3
  • Composition of Distributions
  • HPD, HDI and ROPE
  • Credible and Confidence Intervals

3 Readings

  • What can you expect from this course/specialization?
  • Probabilistic Programming Frameworks
  • Plate Notation

Inferring Distributions with PyMC3

    • 5 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • PyMC3 - I

5 Videos

  • Modeling with a Gaussian Distribution
  • Posterior Predictive Checks
  • Robust Models
  • Hierarchical Models
  • Shrinkage in Hierarchical Models

Regression

    • 6 Videos
Show info about module content

6 Videos

  • Linear Regression
  • Mean-centering for Linear Regression
  • Robust Linear Regression
  • Hierarchical Linear Regression
  • Polynomial Linear Regression
  • Multiple Linear Regression

Classification

    • 8 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • PyMC3 - II

8 Videos

  • Logistic Regression
  • Logistic Regression with PyMC3
  • Decision Boundary for Classification
  • Multiple Logistic Regression
  • Multiclass Logistic Regression
  • Case Study with PyMC3 - I
  • Case Study with PyMC3 - II
  • Case Study with PyMC3 - III

Introduction to metrics

    • 8 Videos
    • 3 Readings
Show info about module content

8 Videos

  • Introduction to Metrics and Tuning
  • Metropolis and HMC
  • Mixing and Potential Scale Reduction Factor
  • Centered and Non-centered Parameterization
  • Assess convergence in PyMC3
  • Forest plots for visualization
  • Autocorrelation and Effective Sample Size
  • Monte Carlo error and Divergences

3 Readings

  • Visualization in Bayesian Workflow
  • Tuning
  • Improved Rhat

Metrics in PyMC3

    • 3 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • PyMC3 - III

3 Videos

  • Diagnosing issues in PyMC3
  • Diagnosing issues in PyMC3 with the multiclass classification problem
  • Debugging in PyMC3

Using PyMC3 to model the disease dynamics of COVID-19

    • 1 Plugin
Show info about module content

Auto Summary

Dive into Bayesian Modeling and Inference with PyMC3 in this foundational course led by Dr. Srijith Rajamohan. Ideal for data science enthusiasts, learners will explore PyMC3 basics, scalable inference techniques, and practical applications using Python and Jupyter notebooks. Offered on Coursera, the course spans 720 minutes and is available through Starter and Professional subscriptions.

Dr. Srijith Rajamohan
Instructor

Dr. Srijith Rajamohan