kandi background

awesome-machine-learning | curated list of awesome Machine Learning frameworks | Machine Learning library

 by   josephmisiti Python Version: Current License: Non-SPDX

 by   josephmisiti Python Version: Current License: Non-SPDX

Download this library from

kandi X-RAY | awesome-machine-learning Summary

awesome-machine-learning is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning applications. awesome-machine-learning has no bugs, it has no vulnerabilities and it has medium support. However awesome-machine-learning build file is not available and it has a Non-SPDX License. You can download it from GitHub.
A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • awesome-machine-learning has a medium active ecosystem.
  • It has 51223 star(s) with 12681 fork(s). There are 3444 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 1 open issues and 70 have been closed. On average issues are closed in 51 days. There are 2 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of awesome-machine-learning is current.
This Library - Support
Best in #Machine Learning
Average in #Machine Learning
This Library - Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • awesome-machine-learning has 0 bugs and 0 code smells.
This Library - Quality
Best in #Machine Learning
Average in #Machine Learning
This Library - Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • awesome-machine-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • awesome-machine-learning code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
This Library - Security
Best in #Machine Learning
Average in #Machine Learning
This Library - Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • awesome-machine-learning has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
This Library - License
Best in #Machine Learning
Average in #Machine Learning
This Library - License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • awesome-machine-learning releases are not available. You will need to build from source code and install.
  • awesome-machine-learning has no build file. You will be need to create the build yourself to build the component from source.
  • awesome-machine-learning saves you 6 person hours of effort in developing the same functionality from scratch.
  • It has 18 lines of code, 0 functions and 1 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
This Library - Reuse
Best in #Machine Learning
Average in #Machine Learning
This Library - Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

Coming Soon for all Libraries!

Currently covering the most popular Java, JavaScript and Python libraries. See a SAMPLE HERE.
kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.

awesome-machine-learning Key Features

Repository's owner explicitly says that "this library is not maintained".

Not committed for a long time (2~3 years).

For a list of free machine learning books available for download, go here.

For a list of professional machine learning events, go here.

For a list of (mostly) free machine learning courses available online, go here.

For a list of blogs and newsletters on data science and machine learning, go here.

For a list of free-to-attend meetups and local events, go here.

awesome-machine-learning Examples and Code Snippets

Community Discussions

Vulnerabilities

No vulnerabilities reported

Install awesome-machine-learning

You can download it from GitHub.
You can use awesome-machine-learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

DOWNLOAD this Library from

Build your Application

Share this kandi XRay Report

Reuse Pre-built Kits with awesome-machine-learning
Reuse Solution Kits and Libraries Curated by Popular Use Cases

Save this library and start creating your kit