Statistics Libraries

67 Components & Libraries

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Statistics Libraries

scikit-learn: machine learning in Python

It is currently maintained by a team of volunteers. scikit-learn requires: You can check the latest sources with the command: The project is currently maintained by a team of volunteers.

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of rea…

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

imbalanced-learn requires the following dependencies: Additionally, imbalanced-learn requires the following optional dependencies: The examples will requires the following additional dependencies: im…

🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.

Boltons can be added to a project in a few ways. There's the obvious one:

A Python based monitoring and tracking tool for Plex Media Server.

This is free software under the GPL v3 open source license. Feel free to do with it what you wish, but any modification must be open sourced. A copy of the license is included.

🌊 Online machine learning in Python

Now let's run the model on the dataset in a streaming fashion. We sequentially interleave predictions and model updates. Meanwhile, we update a performance metric to see how well the model is doing. …

A collection of scientific methods, processes, algorithms, and systems to build stories & models. This roadmap contains 16 Chapters, whether you are a fresher in the field or an experienced professional who wants to transition into Data Science & AI

A collection of scientific methods, processes, algorithms, and systems to build stories & models. This roadmap contains 16 Chapters, whether you are a fresher in the field or an experienced profe…

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

xLearn is developed by high-performance C++ code with careful design and optimizations. Our system is designed to maximize CPU and memory utilization, provide cache-aware computation, and support loc…

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

You can start with this article that I wrote in Heartbeat magazine (on Medium platform): Jupyter notebooks covering a wide range of functions and operations on the topics of NumPy, Pandans, Seaborn, …

Carefully curated resource links for data science in one place

A trove of carefully curated resources and links (on the topics of software, platforms, language, techniques, etc.) related to data science, all in one place. Homework 3

Better GitHub statistics images for your profile, with stats from private repos too

Generate visualizations of GitHub user and repository statistics with GitHub Actions. Visualizations can include data for both private repositories, and for repositories you have contributed to, but …

An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈

These three functionalities enable a variety of use cases for data scientists, machine learning engineers, and data engineers: From here, you can quickly log a dataset: And voilà, you now have a whyl…

Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

There are eight subjects in the curriculum, organized into four subject areas. See the "Machine Learning House" section below for detail on why these are the essential foundational subject areas: Lat…

The official repository for ROOT: analyzing, storing and visualizing big data, scientifically

The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data in a very efficient way. Having the data defined as a set of objects, spe…

Open source time series library for Python

See some examples and documentation below. PyFlux is still only alpha software; this means you use it at your own risk, that test coverage is still in need of expansion, and also that some modules ar…

Data Science Roadmap from A to Z

Pandas

Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way

“Study hard what interests you the most in the most undisciplined, irreverent and original manner possible.” ― Richard Feynman Math literacy, including proficiency in Linear Algebra and Statistics,is…

Multi-class confusion matrix library in Python

Fig1. ConfusionMatrix Block Diagram After that, two scores are calculated for each confusion matrices, overall and class-based. The overall score is the average of the score of seven overall benchmar…

Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

This project contains Jupyter notebooks of many the algorithms presented in Christopher Bishop's Pattern Recognition and Machine Learning book, as well as replicas for many of the graphs presented in…

A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.

A comprehensive list of +180 YouTube Channels (in no particular order) for Data Science, Data Engineering, Machine Learning, Deep Learning, Artificial Intelligence, Computer Science, programming, sof…

Curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome

Awesome software projects sub-categorized by focus. To the extent possible under law, all contributors have waived all copyright and related or neighboring rights to this work.

DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration

Dataframes are used for statistics, machine-learning, and data manipulation/exploration. You can think of a Dataframe as an excel spreadsheet. This package is designed to be light-weight and intuitiv…

Notes On Using Data Science & Artificial Intelligence To Fight For Something That Matters.

Code, exercises and tutorials of my personal blog ! 📝

The repository is organized the following way : For the moment, these cheat sheets are written manually. I'd like to create a visual content later that would both dive in the maths and illustrate cle…

Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.

A collection of Python demos for spatial data analytics, geostatistics and machine learning methods. Please cite as: I use these demonstrations for: Other resources that you might be interested in in…