Databricks - Introduction to PyMC3 for Bayesian Modeling and Inference
- Offered byCoursera
Introduction to PyMC3 for Bayesian Modeling and Inference at Coursera Overview
Duration | 12 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to PyMC3 for Bayesian Modeling and Inference at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 3 in the Introduction to Computational Statistics for Data Scientists Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level 1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn. 2. Course 1 &; 2 in this Specialization.
- Approx. 12 hours to complete
- English Subtitles: English
Introduction to PyMC3 for Bayesian Modeling and Inference at Coursera Course details
- 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.
Introduction to PyMC3 for Bayesian Modeling and Inference at Coursera Curriculum
Introduction to PyMC3 - Part 1
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
Modeling with a Gaussian Distribution
Posterior Predictive Checks
Robust Models
Hierarchical Models
Shrinkage in Hierarchical Models
What can you expect from this course/specialization?
Probabilistic Programming Frameworks
Plate Notation
PyMC3 - I
Introduction to PyMC3 - Part 2
Linear Regression
Mean-centering for Linear Regression
Robust Linear Regression
Hierarchical Linear Regression
Polynomial Linear Regression
Multiple Linear Regression
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
PyMC3 - II
Metrics in PyMC3
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
Diagnosing issues in PyMC3
Diagnosing issues in PyMC3 with the multiclass classification problem
Debugging in PyMC3
Visualization in Bayesian Workflow
Tuning
Improved Rhat
PyMC3 - III
Modeling of COVID-19 cases using PyMC3
Introduction to PyMC3 for Bayesian Modeling and Inference at Coursera Admission Process
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