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.
このコースについて
3,102 最近の表示
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
初級レベル
1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
2. Course 1 & 2 in this Specialization.
約12時間で修了
英語
字幕：英語
学習内容
1. The PyMC3/ArViz framework for Bayesian modeling and inference
2. Build real-world models using PyMC3 and assess the quality of your models
習得するスキル
- Bayesian Inference
- Python Programming
- Monte Carlo Method
- PyMC3
- Scipy
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
初級レベル
1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
2. Course 1 & 2 in this Specialization.
約12時間で修了
英語
字幕：英語
提供：
データプリックス
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.
シラバス - 本コースの学習内容
2時間で修了
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
2時間で修了
12件のビデオ （合計48分）, 3 学習用教材, 1 個のテスト
1時間で修了
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
1時間で修了
14件のビデオ （合計47分）
5時間で修了
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
5時間で修了
11件のビデオ （合計28分）, 3 学習用教材, 1 個のテスト
3時間で修了
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.
3時間で修了
Introduction to Computational Statistics for Data Scientists専門講座について
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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