├── .gitattributes ├── README.md ├── images ├── 12algorithmseverydatascientistshouldknow.jpg ├── NeuralNetworkZo19High.png ├── Numpy.png ├── bokeh.png ├── calculus.png ├── datacamp-python.png ├── dataiku.png ├── linear-algebra.png ├── machine-learning-cheet-sheet-2.png ├── matplotlib.png ├── microsoft-azure-algorithmn.png ├── numpy1.png ├── numpy2.png ├── pandas1-1.png ├── pandas1-4.png ├── probability.png ├── python1.png ├── python2.png ├── qrcode-search-caiyongji.png ├── scikit.png ├── spaCy.png └── stats.png └── pdfs ├── Calculus_Cheat_Sheet_All_Reduced.pdf ├── ML-basics.pdf ├── Master Machine Learning Algorithms - Discover how they work.pdf ├── Numpy_Python_Cheat_Sheet.pdf ├── Scikit_Learn_Cheat_Sheet_Python.pdf ├── azure-machine-learning-algorithm-cheat-sheet-nov2019.pdf ├── bokeh.pdf ├── datacamp-matplotlib.pdf ├── datacamp-pands.pdf ├── datacamp-python.pdf ├── datacamp-spacy.pdf ├── document.pdf ├── linear_algebra_in_4_pages.pdf ├── numpy-cheat-sheet.pdf ├── numpy.pdf ├── pandas.pdf ├── probability_cheatsheet.pdf ├── python.pdf ├── stats_handout.pdf └── 欢迎关注公众号caiyongji获取无水印pdf文件.txt /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MachineLearningCheatsheets 2 | 机器学习速查表(cheatsheet)资源汇总 3 | 4 | # 求关注 # 5 | 6 | 我的机器学习相关原创文章: [Caiyongji's Blog - TAGS](http://blog.caiyongji.com/archive.html) 7 | 更多机器学习相关资源、原创文章、原创教程请关注我的公众号：**caiyongji** 8 | 9 | ![](images/qrcode-search-caiyongji.png) 10 | 11 | 目录 12 | ----------------- 13 | 14 | - [机器学习](#机器学习) 15 | - [神经网络架构](#神经网络架构) 16 | - [Azure机器学习算法](#Azure-机器学习算法) 17 | - [SAS 机器学习算法](#SAS-机器学习算法) 18 | - [The world of machine learning algorithms](#The-world-of-machine-learning-algorithms) 19 | - [data iku 机器学习算法](#data-iku-机器学习算法) 20 | - [Python](#Python) 21 | - [Python 速查表](#Python-速查表) 22 | - [Python 导入数据](#Python-导入数据) 23 | - [Numpy](#Numpy) 24 | - [Pandas](#Pandas) 25 | - [Matplotlib](#Matplotlib) 26 | - [Scikit-Learn](#Scikit-Learn) 27 | - [spaCy](#spaCy) 28 | - [Bokeh](#Bokeh) 29 | - [数学](#数学) 30 | - [概率论](#概率论) 31 | - [线性代数](#线性代数) 32 | - [统计学](#统计学) 33 | - [微积分](#微积分) 34 | 35 | 36 | 37 | # 机器学习 # 38 | 39 | ## 神经网络架构 ## 40 | 41 | 神经网络结构速查表，包括感知机、前馈神经网络、径向基函数网络、循环神经网络等。 42 | 43 | ![](images/NeuralNetworkZo19High.png) 44 | 45 | ## Azure 机器学习算法 ## 46 | 47 | Microsoft Azure机器学习拥有来自分类，推荐系统，群集，异常检测，回归和文本分析系列的大型算法库。每个都旨在解决不同类型的机器学习问题。 48 | 49 | 每种机器学习算法都有自己的风格或归纳偏差。对于特定问题，几种算法可能是合适的，并且一种算法可能比其他算法更合适。但是，并非总是可能事先知道哪种方法最合适。在这种情况下，速查表中会同时列出几种算法。适当的策略是尝试一种算法，如果结果仍不令人满意，则尝试其他算法。 50 | 51 | ![](images/microsoft-azure-algorithmn.png) 52 | 53 | ## SAS 机器学习算法 ## 54 | 55 | 该资源主要是为对识别和应用机器学习算法来解决他们感兴趣的问题感兴趣的初学者到中级数据科学家或分析人员而设计的。 56 | 即使是经验丰富的数据科学家，也无法在尝试不同算法之前就确定哪种算法性能最佳。我们不提倡一种完成的方法，但是我们确实希望根据一些明确的因素提供有关哪些算法首先要尝试的指导。 57 | 58 | ![](images/machine-learning-cheet-sheet-2.png) 59 | 60 | ## The world of machine learning algorithms ## 61 | 62 | 这是一个非常不错的信息图，展示了机器学习算法类别的基本类型。 63 | 64 | ![](images/12algorithmseverydatascientistshouldknow.jpg) 65 | 66 | ## data iku 机器学习算法 ## 67 | 68 | 我们(data iku)对预测分析中使用的顶级算法进行了简要总结，您可以在下面看到。 69 | 70 | ![](images/dataiku.png) 71 | 72 | # Python # 73 | 74 | ## Python 速查表 ## 75 | 76 | ![](images/python1.png) 77 | 78 | ![](images/python2.png) 79 | 80 | ## Python 导入数据 ## 81 | 82 | ![](images/datacamp-python.png) 83 | 84 | ## Numpy ## 85 | 86 | NumPy支持大量的维度数组与矩阵运算，是针对数组运算的Python库。我曾经在[前置机器学习系列](https://mp.weixin.qq.com/mp/appmsgalbum?__biz=MzUxMjU4NjI4MQ==&action=getalbum&album_id=1627166768236412929&scene=173&from_msgid=2247484075&from_itemidx=1&count=3#wechat_redirect)里讲过[前置机器学习（三）：30分钟掌握常用NumPy用法](http://blog.caiyongji.com/2020/12/06/pre-ml-numpy-3.html)。 87 | 88 | ![](images/Numpy.png) 89 | 90 | ![](images/Numpy1.png) 91 | 92 | ![](images/Numpy2.png) 93 | 94 | ## Pandas ## 95 | 96 | Pandas提供快速，灵活和富于表现力的数据结构，是强大的数据分析Python库。 97 | 我曾经写过近万字的文章介绍pandas。 98 | 我曾经在[前置机器学习系列](https://mp.weixin.qq.com/mp/appmsgalbum?__biz=MzUxMjU4NjI4MQ==&action=getalbum&album_id=1627166768236412929&scene=173&from_msgid=2247484075&from_itemidx=1&count=3#wechat_redirect)里讲过[前置机器学习（四）：一文掌握Pandas用法](http://blog.caiyongji.com/2020/12/13/pre-ml-pandas-4.html)。 99 | 100 | ![](images/pandas1-1.png) 101 | 102 | ![](images/pandas1-4.png) 103 | 104 | 篇幅关系，以上仅为部分`pandas`速查表图片，请参考本项目./pdfs路径下原文件。 105 | 106 | ## Matplotlib ## 107 | 108 | 关于matplotlib的相关文章收录于[前置机器学习系列](https://mp.weixin.qq.com/mp/appmsgalbum?__biz=MzUxMjU4NjI4MQ==&action=getalbum&album_id=1627166768236412929&scene=173&from_msgid=2247484075&from_itemidx=1&count=3#wechat_redirect)。 109 | 110 | ![](images/matplotlib.png) 111 | 112 | ## Scikit-Learn ## 113 | 114 | ![](images/scikit.png) 115 | 116 | ## spaCy ## 117 | 118 | ![](images/spaCy.png) 119 | 120 | ## Bokeh ## 121 | 122 | ![](images/bokeh.png) 123 | 124 | 125 | # 数学 # 126 | 127 | ## 概率论 ## 128 | 129 | ![](images/probability.png) 130 | 131 | 篇幅关系，以上仅为部分`概率论`速查表图片，请参考本项目./pdfs路径下原文件。 132 | 133 | ## 线性代数 ## 134 | 135 | ![](images/linear-algebra.png) 136 | 篇幅关系，以上仅为部分`线性代数`速查表图片，请参考本项目./pdfs路径下原文件。 137 | 138 | ## 统计学 ## 139 | 140 | ![](images/stats.png) 141 | 142 | 篇幅关系，以上仅为部分`统计学`速查表图片，请参考本项目./pdfs路径下原文件。 143 | 144 | ## 微积分 ## 145 | 146 | ![](images/calculus.png) 147 | 148 | 篇幅关系，以上仅为部分`微积分`速查表图片，请参考本项目./pdfs路径下原文件。 149 | -------------------------------------------------------------------------------- /images/12algorithmseverydatascientistshouldknow.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/caiyongji/MachineLearningCheatsheets/dc41e2bbf0345fb5e38acd227a7720201dcc225a/images/12algorithmseverydatascientistshouldknow.jpg 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