josephmisiti/awesome-machine-learning

Analysis updated 2026-06-20

★ 72,406PythonAudience · researcherComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((repo))
    What it Does
      Curated ML library list
      Language-organized index
      Subfield navigation
    Languages Covered
      Python
      C++ and Java
      JavaScript and R
      Julia and others
    Subfields
      Computer vision
      NLP
      Deep learning
      Reinforcement learning
    Audience
      ML researchers
      Data scientists
    Use Cases
      Library discovery
      Ecosystem onboarding

mindmap root((repo)) What it Does Curated ML library list Language-organized index Subfield navigation Languages Covered Python C++ and Java JavaScript and R Julia and others Subfields Computer vision NLP Deep learning Reinforcement learning Audience ML researchers Data scientists Use Cases Library discovery Ecosystem onboarding

Click or tap to explore — scroll the page freely

What do people build with it?

USE CASE 1

Find the best Python library for a specific ML subfield like NLP, computer vision, or reinforcement learning before starting a project.

USE CASE 2

Survey what machine learning tools exist in Go, Rust, or Julia when you want to avoid Python for performance reasons.

USE CASE 3

Onboard a new team member to the ML ecosystem with a structured, language-organized overview of available tools.

What is it built with?

PythonC++JavaJavaScriptRJulia

How does it compare?

	josephmisiti/awesome-machine-learning	z4nzu/hackingtool	python/cpython
Stars	72,406	72,250	72,593
Language	Python	Python	Python
Setup difficulty	easy	moderate	hard
Complexity	1/5	2/5	5/5
Audience	researcher	developer	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

No license information is specified in the repository description.

In plain English

Awesome Machine Learning is a curated reference list of machine learning frameworks, libraries, and software tools, organized by programming language. The problem it solves is discovery: the machine learning ecosystem is enormous and fragmented across many languages and subfields, and finding the right library for a specific task in a specific language can be time-consuming. This list gathers vetted options in one place so developers and researchers can quickly find what exists without searching through countless individual repositories. The list is organized by programming language, covering a very wide range including Python, C++, Java, JavaScript, Julia, Go, R, Scala, Ruby, Rust, and many others. Within each language, entries are grouped by ML subfield such as general-purpose machine learning, computer vision, natural language processing, data analysis, deep learning, and reinforcement learning. Each entry is a link to the relevant library or tool with a brief description. Companion files cover free books, online courses, blogs, and events related to machine learning. The repository notes that as of April 2026 it requires human confirmation for pull requests due to an influx of LLM-generated contributions. You would use this repository when you are starting a new ML project and want to survey what libraries are available in your language of choice, when you are researching tools in a specific subfield, or when onboarding someone new to the ML ecosystem. It is not software you run, it is a reference document in Markdown format. The repository is part of the broader "awesome" list convention on GitHub, where curated topic lists are shared as structured Markdown files.

Copy-paste prompts

Prompt 1

Using the awesome-machine-learning list, what are the top Python libraries for natural language processing and which one should I start with for text classification?

Prompt 2

I'm building a computer vision app in Python. According to the awesome-machine-learning list, what are my main library options and how do they differ?

Prompt 3

I need a reinforcement learning framework in Python. What does the awesome-machine-learning list recommend, and which works best with PyTorch?

Prompt 4

What machine learning libraries does the awesome-machine-learning list cover for JavaScript developers who want to run models in the browser?

Frequently asked questions

What is awesome-machine-learning?

A curated reference list of machine learning libraries and tools organized by programming language and subfield, making it easy to find the right library for any ML task without sifting through thousands of repos.

What language is awesome-machine-learning written in?

Mainly Python. The stack also includes Python, C++, Java.

What license does awesome-machine-learning use?

No license information is specified in the repository description.

How hard is awesome-machine-learning to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is awesome-machine-learning for?

Mainly researcher.

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