https://github.com/jacksu/machine-learning
从零基础开始机器学习之旅
https://github.com/jacksu/machine-learning
anomalydetection awesome deep-learning kaggle machine-learning ml nlp pandas python sklearn tensorflow
Last synced: 2 months ago
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从零基础开始机器学习之旅
- Host: GitHub
- URL: https://github.com/jacksu/machine-learning
- Owner: jacksu
- License: mit
- Created: 2017-01-19T11:40:06.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2018-11-15T12:39:05.000Z (about 7 years ago)
- Last Synced: 2024-05-20T00:12:16.887Z (over 1 year ago)
- Topics: anomalydetection, awesome, deep-learning, kaggle, machine-learning, ml, nlp, pandas, python, sklearn, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 263 KB
- Stars: 237
- Watchers: 15
- Forks: 88
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- be-a-professional-programmer - awesome-machine-learning - 机器学习资料 (资料篇 / GitHub篇)
README
该工程主要包含机器学习学习过程中收集的相关资料和实践代码。
## 资料主要包括：
> * [Machine Learning Glossary](https://developers.google.com/machine-learning/glossary/)---This glossary defines general machine learning terms as well as terms specific to TensorFlow.
> * [awesome-machine-learning-on-source-code](https://github.com/src-d/awesome-machine-learning-on-source-code)---Interesting links & research papers related to Machine Learning applied to source code
> * [state-of-the-art-result-for-machine-learning-problems](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems)---This repository provides state of the art (SoTA) results for all machine learning problems.
> * [awesome](awesome.md)
> * [时间序列数据分析](TimeSeriesAnalysis.md)
> * [自然语言处理NLP](NLP.md)
> * [基本机器学习算法相关资料](algorithm.md)
> * [深度学习相关资料](DeepLearning.md)
> * [tensorflow相关资料](tensorflow.md)
> * [kaggle相关资料](kaggle.md)
> * [jupyter相关资料](jupyter.md)
> * [MachinLearningOnSpark](MachineLearningOnSpark.md)
> * [实践代码](/src/ml)
## cheat sheet
### ML
[machine learning cheat sheet](https://github.com/kailashahirwar/cheatsheets-ai)
### numpy
[numpy cheat sheet](https://www.dataquest.io/blog/numpy-cheat-sheet/)
### pandas
[pandas cheat sheet](https://www.dataquest.io/blog/pandas-cheat-sheet/)
[实操 | 内存占用减少高达90%，还不用升级硬件？没错，这篇文章教你妙用Pandas轻松处理大规模数据](http://blog.csdn.net/wemedia/details.html?id=43144)
## scikit learn
[scikit cheat sheet](http://scikit-learn.org/stable/tutorial/machine_learning_map/)
## charts
[pyecharts](https://github.com/chenjiandongx/pyecharts)
**代码**
实践代码主要基于`python 3.6.1`，依赖的module有：
> * numpy+mkl(最好使用whl安装)
> * scipy（最好使用whl安装）
> * pandas
> * matplotlib & seaborn
> * ipython
> * jupyter
**whl url**
[python module lib whl](http://www.lfd.uci.edu/~gohlke/pythonlibs/)