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.
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立即開始，按照自己的計劃學習。
初級
1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
2. Course 1 & 2 in this Specialization.
完成時間大約為12 小時
英語（English）
字幕：英語（English）
您將學到的內容有
1. The PyMC3/ArViz framework for Bayesian modeling and inference
2. Build real-world models using PyMC3 and assess the quality of your models
您將獲得的技能
- Bayesian Inference
- Python Programming
- Monte Carlo Method
- PyMC3
- Scipy
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
初級
1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
2. Course 1 & 2 in this Specialization.
完成時間大約為12 小時
英語（English）
字幕：英語（English）
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数据块
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授課大綱 - 您將從這門課程中學到什麼
完成時間為 2 小時
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
完成時間為 2 小時
12 個視頻 （總計 48 分鐘）, 3 個閱讀材料, 1 個測驗
完成時間為 1 小時
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
完成時間為 1 小時
14 個視頻 （總計 47 分鐘）
完成時間為 5 小時
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
完成時間為 5 小時
11 個視頻 （總計 28 分鐘）, 3 個閱讀材料, 1 個測驗
完成時間為 3 小時
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.
完成時間為 3 小時
關於 Introduction to Computational Statistics for Data Scientists 專項課程
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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