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.
Ce cours fait partie de la Spécialisation Introduction to Computational Statistics for Data Scientists
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Dates limites flexibles
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Certificat partageable
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100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 3 sur 3 dans le
Niveau débutant
1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
2. Course 1 & 2 in this Specialization.
Approx. 12Â heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
1. The PyMC3/ArViz framework for Bayesian modeling and inference
2. Build real-world models using PyMC3 and assess the quality of your models
Compétences que vous acquerrez
- Bayesian Inference
- Python Programming
- Monte Carlo Method
- PyMC3
- Scipy
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 3 sur 3 dans le
Niveau débutant
1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
2. Course 1 & 2 in this Specialization.
Approx. 12Â heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Databricks
Databricks is the data and AI company. Founded by the creators of Apache Spark™, Delta Lake and MLflow, organizations like Comcast, Condé Nast, Nationwide and H&M rely on Databricks’ open and unified platform to enable data engineers, scientists and analysts to collaborate and innovate faster.
Programme de cours : ce que vous apprendrez dans ce cours
2Â heures pour terminer
Introduction to PyMC3 - Part 1
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
2Â heures pour terminer
12 vidéos (Total 48 min), 3 lectures, 1 quiz
1Â heure pour terminer
Introduction to PyMC3 - Part 2
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
1Â heure pour terminer
14 vidéos (Total 47 min)
5Â heures pour terminer
Metrics in PyMC3
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
5Â heures pour terminer
11 vidéos (Total 28 min), 3 lectures, 1 quiz
3Â heures pour terminer
Modeling of COVID-19 cases using PyMC3
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.
3Â heures pour terminer
À propos du Spécialisation Introduction to Computational Statistics for Data Scientists
The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST). What it does cover is:
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