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gimseng

99-ML-Learning-Projects

A list of 99 machine learning projects for anyone interested to learn from coding and building projects

TutorialsML/AI fundamentalsJupyter Notebook
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创建于 2020-07-11 · 更新于 2026-10-15 · 今日第 11128 名
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gimsengAjayKhalsagngai2026

gimseng/99-ML-Learning-Projects 排名第 11,128，有 1,211 个星标。

同类别比较

gimseng/99-ML-Learning-Projects 排名第 11,128，此处有 8 个同类条目；imfing/keras-flask-deploy-webapp 排名第 11,129。

同类别比较
名称排名指标
hanbt/learn_dl11,075
语言:
TeX
星标数:
1,225
分叉数:
971
简介:
Deep learning algorithms source code for beginners
ml4a/ml4a-ofx11,080
语言:
C++
星标数:
1,224
分叉数:
272
简介:
A collection of openFrameworks apps for working with machine learning
hans0809/MiniMind-in-Depth11,096
星标数:
1,220
分叉数:
92
简介:
轻量级大语言模型MiniMind的源码解读，包含tokenizer、RoPE、MoE、KV Cache、pretraining、SFT、LoRA、DPO等完整流程
wandb/examples11,122
语言:
Jupyter Notebook
星标数:
1,213
分叉数:
304
简介:
Example deep learning projects that use wandb's features.
imfing/keras-flask-deploy-webapp11,129
语言:
JavaScript
星标数:
1,211
分叉数:
458
简介:
:smiley_cat: Pretty & simple image classifier app template. Deploy your own trained model or pre-trained model (VGG, ResNet, Densenet) to a web app using Flask in 10 minutes.
iannuttall/claude-sessions11,136
星标数:
1,209
分叉数:
137
简介:
Custom slash commands for Claude Code that provide comprehensive development session tracking and documentation
roatienza/Deep-Learning-Experiments11,143
语言:
Jupyter Notebook
星标数:
1,207
分叉数:
767
简介:
Videos, notes and experiments to understand deep learning
6174/comflowy11,153
语言:
MDX
星标数:
1,204
分叉数:
70
简介:
Unleash endless possibilities with ComfyUI and Stable Diffusion, committed to crafting refined AI-Gen tools and cultivating a vibrant community for both developers and users.
关键参数与规格
关键参数与规格
参数值
语言Jupyter Notebook
类别Tutorials
子类别ML/AI fundamentals
星标数1,211
分叉数232
贡献者16
创建日期2020-07-11
更新日期2026-10-15
1 天星标数0
7 天星标数3
排名11,128
上次排名4,084
排名变化-7,044
7 天星标排名7,022
上次 7 天星标排名7,045
7 天星标排名变化23
总星标排名9,628
上次总星标排名9,616
总星标排名变化-12
README 摘录

99-ML-Learning-Projects A list of 99 machine learning projects for anyone interested to learn machine learning from coding and building projects. Our working philosophy is to provide a curated repo for anyone to contribute a cool/fun exercise and solution that is useful for anyone (including themselves) in their journey of learning machine learning. Getting Started The format is roughly the following: 1. Propose an exercise by creating an issue ticket and write what you think is an

useful coding exercise for certain concepts. If enough people are interested in that issue ticket, hopefully either you or someone else will write the exercise statement properly similar to the style of a lab exercise/homework question. Then someone will fork the repo, write up their solution, with a bit of polish and documentation, submit a pull request. Please see general contribution guidelines for more details on how to contribute solutions. Some of us will scrutinize the codes, review, make

suggestions and eventually include (merge) them into the main project repo. At anytime, someone can repeat suggest improvements/changes to 3-4 above for a particular exercise. This is done by creating an issue ticket for improvement/enhancement. Finally, repeat 1-5 indefinitely till we hit 99/99 projects. Please abide by code of conduct guidelines to have an open and friendly open source collaboration. Goal: 99 Projects Current: 10 Projects Table of Contents General-Purpose Machine Learning [Linear Regression

[Beginner]]( [Titanic Survival Prediction [Beginner]]( [kNN from Scratch[Beginner]]( [kNN from Sklearn [Beginner]]( [Bagging and boosting ensemble methods [Intermediate]]( Computer Vision [MNIST Handwriting Digit Recognition [Intermediate]]( Natural Language Processing [Sentiment analysis [Intermediate]]( [Text-generation neural network model (with LSTM) [Advanced]]( Bayesian Naive Bayes Classification Misc/Mix Models Employee Attrition & Performance Refreshers/Cheatsheets Numpy Pandas Dependencies Some of the libraries (and their versions) we are using: Python (>= 3. 1) Help and Support If you want to get in touch with us

, say hi on our discord/gitter chatroom: Discord: Gitter: Recent Contributors Credit: This project is inspired by Unnit Metaliya’s answer on quora: For credits, these are the two repos (one for C and one for React) where I got the idea from: License This repo is covered under The MIT License.

常见问题

记录了多少星标和分叉？

gimseng/99-ML-Learning-Projects 有 1,211 个星标和 232 个分叉。

仓库排名是多少？

gimseng/99-ML-Learning-Projects 排名第 11,128。

记录的语言和类别是什么？

gimseng/99-ML-Learning-Projects 有 1,211 个星标，语言为 Jupyter Notebook，类别为 Tutorials。

哪个仓库排名最接近？

gimseng/99-ML-Learning-Projects 与 imfing/keras-flask-deploy-webapp 比较，后者排名第 11,129，有 1,211 个星标。

README

99-ML-Learning-Projects

A list of 99 machine learning projects for anyone interested to learn machine learning from coding and building projects.

Our working philosophy is to provide a curated repo for anyone to contribute a cool/fun exercise and solution that is useful for anyone (including themselves) in their journey of learning machine learning.

Getting Started

The format is roughly the following:

  1. Propose an exercise by creating an issue ticket and write what you think is an useful coding exercise for certain concepts.

  2. If enough people are interested in that issue ticket, hopefully either you or someone else will write the exercise statement properly similar to the style of a lab exercise/homework question.

  3. Then someone will fork the repo, write up their solution, with a bit of polish and documentation, submit a pull request. Please see general contribution guidelines for more details on how to contribute solutions.

  4. Some of us will scrutinize the codes, review, make suggestions and eventually include (merge) them into the main project repo.

  5. At anytime, someone can repeat suggest improvements/changes to 3-4 above for a particular exercise. This is done by creating an issue ticket for improvement/enhancement. One can then repeat 3-4.

  6. Finally, repeat 1-5 indefinitely till we hit 99/99 projects.

Please abide by code of conduct guidelines to have an open and friendly open source collaboration.

Goal: 99 Projects

Current: 10 Projects

Table of Contents

General-Purpose Machine Learning

Computer Vision

Natural Language Processing

Bayesian

Misc/Mix Models

Refreshers/Cheatsheets

Dependencies

Some of the libraries (and their versions) we are using:

  • Python (>= 3.6)
  • NumPy (>= 1.18.5)
  • Pandas (>= 1.0.5)
  • Matplotlib (>= 3.2.2)
  • Seaborn (>= 0.10.1)
  • Scikit-learn (>= 0.22.2)
  • Tensorflow (>= 2.2.0)
  • PyTorch (>= 1.5.1)

Help and Support

If you want to get in touch with us, say hi on our discord/gitter chatroom:

Recent Contributors

Credit:

This project is inspired by Unnit Metaliya’s answer on quora: https://qr.ae/pNK0FW

For credits, these are the two repos (one for C and one for React) where I got the idea from:

License

This repo is covered under The MIT License.

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