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MIT licensehttps://github.com/fengdu1127/tmp
https://github.com/fengdu1127/tmp#机器学习100天
Avik-Jainhttps://github.com/Avik-Jain/100-Days-Of-ML-Code
这里https://github.com/MachineLearning100/100-Days-Of-ML-Code/tree/master/datasets
规范https://github.com/fengdu1127/tmp/blob/master/Translation%20specification.MD
FAQhttps://github.com/fengdu1127/tmp/blob/master/FAQ.MD
https://github.com/fengdu1127/tmp#目录
数据预处理https://github.com/fengdu1127/tmp#%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86--%E7%AC%AC1%E5%A4%A9
简单线性回归https://github.com/fengdu1127/tmp#%E7%AE%80%E5%8D%95%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92--%E7%AC%AC2%E5%A4%A9
多元线性回归https://github.com/fengdu1127/tmp#%E5%A4%9A%E5%85%83%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92--%E7%AC%AC3%E5%A4%A9
逻辑回归https://github.com/fengdu1127/tmp#%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92--%E7%AC%AC4%E5%A4%A9
k近邻法(k-NN)https://github.com/fengdu1127/tmp#k%E8%BF%91%E9%82%BB%E6%B3%95k-nn--%E7%AC%AC7%E5%A4%A9
支持向量机(SVM)https://github.com/fengdu1127/tmp#%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BAsvm--%E7%AC%AC12%E5%A4%A9
决策树https://github.com/fengdu1127/tmp#%E5%86%B3%E7%AD%96%E6%A0%91--%E7%AC%AC23%E5%A4%A9
随机森林https://github.com/fengdu1127/tmp#%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97--%E7%AC%AC33%E5%A4%A9
K-均值聚类https://github.com/fengdu1127/tmp#k-%E5%9D%87%E5%80%BC%E8%81%9A%E7%B1%BB--%E7%AC%AC43%E5%A4%A9
层次聚类https://github.com/fengdu1127/tmp#%E5%B1%82%E6%AC%A1%E8%81%9A%E7%B1%BB--%E7%AC%AC54%E5%A4%A9
https://github.com/fengdu1127/tmp#数据预处理--第1天
数据预处理实现https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%201_Data_Preprocessing.md
https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%201.jpg
https://github.com/fengdu1127/tmp#简单线性回归--第2天
简单线性回归实现https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%202_Simple_Linear_Regression.md
https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%202.jpg
https://github.com/fengdu1127/tmp#多元线性回归--第3天
多元线性回归实现https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%203_Multiple_Linear_Regression.md
https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%203.png
https://github.com/fengdu1127/tmp#逻辑回归--第4天
https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%204.jpg
https://github.com/fengdu1127/tmp#逻辑回归--第5天
https://github.com/fengdu1127/tmp#逻辑回归--第6天
逻辑回归实现https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%206_Logistic_Regression.md
https://github.com/fengdu1127/tmp#k近邻法k-nn--第7天
https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%207.jpg
https://github.com/fengdu1127/tmp#逻辑回归背后的数学--第8天
这篇文章https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc
https://github.com/fengdu1127/tmp#支持向量机svm--第9天
https://github.com/fengdu1127/tmp#支持向量机和k近邻法--第10天
https://github.com/fengdu1127/tmp#k近邻法k-nn--第11天
K近邻法(k-NN)实现https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2011_K-NN.md
https://github.com/fengdu1127/tmp#支持向量机svm--第12天
https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%2012.jpg
https://github.com/fengdu1127/tmp#支持向量机svm--第13天
SVM实现https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2013_SVM.md
https://github.com/fengdu1127/tmp#支持向量机svm的实现--第14天
此处https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2013_SVM.py
此处https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2013_SVM.ipynb
https://github.com/fengdu1127/tmp#朴素贝叶斯分类器naive-bayes-classifier和黑盒机器学习black-box-machine-learning--第15天
Bloomberghttps://bloomberg.github.io/foml/#home
https://github.com/fengdu1127/tmp#通过内核技巧实现支持向量机--第16天
https://github.com/fengdu1127/tmp#在coursera开始深度学习的专业课程--第17天
https://github.com/fengdu1127/tmp#继续coursera上的深度学习专业课程--第18天
https://github.com/fengdu1127/tmp#学习问题和yaser-abu-mostafa教授--第19天
https://github.com/fengdu1127/tmp#深度学习专业课程2--第20天
https://github.com/fengdu1127/tmp#网页搜罗--第21天
https://github.com/fengdu1127/tmp#学习还可行吗--第22天
https://github.com/fengdu1127/tmp#决策树--第23天
https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%2023%20-%20Chinese.jpg
https://github.com/fengdu1127/tmp#统计学习理论的介绍--第24天
https://github.com/fengdu1127/tmp#决策树--第25天
决策树实现https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2025_Decision_Tree.md
https://github.com/fengdu1127/tmp#跳到复习线性代数--第26天
3Blue1Brownhttps://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw
这里https://space.bilibili.com/88461692/#/channel/detail?cid=9450
https://github.com/fengdu1127/tmp#跳到复习线性代数--第27天
这里https://space.bilibili.com/88461692/#/channel/detail?cid=9450
https://github.com/fengdu1127/tmp#跳到复习线性代数--第28天
这里https://space.bilibili.com/88461692/#/channel/detail?cid=9450
https://github.com/fengdu1127/tmp#跳到复习线性代数--第29天
这里https://space.bilibili.com/88461692/#/channel/detail?cid=9450
https://github.com/fengdu1127/tmp#微积分的本质--第30天
这里https://space.bilibili.com/88461692/#/channel/detail?cid=13407
https://github.com/fengdu1127/tmp#微积分的本质--第31天
这里https://space.bilibili.com/88461692/#/channel/detail?cid=13407
https://github.com/fengdu1127/tmp#微积分的本质--第32天
这里https://space.bilibili.com/88461692/#/channel/detail?cid=13407
https://github.com/fengdu1127/tmp#随机森林--第33天
https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%2033.png
https://github.com/fengdu1127/tmp#随机森林--第34天
随机森林实现https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2034_Random_Forests.md
https://github.com/fengdu1127/tmp#什么是神经网络--深度学习第1章--第-35天
这里https://space.bilibili.com/88461692/#/channel/detail?cid=26587
https://github.com/fengdu1127/tmp#梯度下降法神经网络如何学习--深度学习第2章--第36天
这里https://space.bilibili.com/88461692/#/channel/detail?cid=26587
https://github.com/fengdu1127/tmp#反向传播法究竟做什么--深度学习第3章--第37天
这里https://space.bilibili.com/88461692/#/channel/detail?cid=26587
https://github.com/fengdu1127/tmp#反向传播法演算--深度学习第4章--第38天
这里https://space.bilibili.com/88461692/#/channel/detail?cid=26587
https://github.com/fengdu1127/tmp#第1部分--深度学习基础pythontensorflow和keras--第39天
这里https://www.youtube.com/watch?v=wQ8BIBpya2k&t=19s&index=2&list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN
notebookhttps://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2039.ipynb
https://github.com/fengdu1127/tmp#第2部分--深度学习基础pythontensorflow和keras--第40天
这里https://www.youtube.com/watch?v=wQ8BIBpya2k&t=19s&index=2&list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN
notebookhttps://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2040.ipynb
https://github.com/fengdu1127/tmp#第3部分--深度学习基础pythontensorflow和keras--第41天
这里https://www.youtube.com/watch?v=wQ8BIBpya2k&t=19s&index=2&list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN
notebookhttps://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2041.ipynb
https://github.com/fengdu1127/tmp#第4部分--深度学习基础pythontensorflow和keras--第42天
这里https://www.youtube.com/watch?v=wQ8BIBpya2k&t=19s&index=2&list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN
notebookhttps://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2042.ipynb
https://github.com/fengdu1127/tmp#k-均值聚类--第43天
作者网站http://www.avikjain.me/
动画http://shabal.in/visuals/kmeans/6.html
https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%2043.jpg
https://github.com/fengdu1127/tmp#k-均值聚类--第44天
https://github.com/fengdu1127/tmp#深入研究--numpy--第45天
这里https://github.com/jakevdp/PythonDataScienceHandbook
高清中文版pdfhttps://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Other%20Docs/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E6%89%8B%E5%86%8C.zip
2 NumPy入门https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.00-Introduction-to-NumPy.ipynb
2.1 理解Python中的数据类型https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.01-Understanding-Data-Types.ipynb
2.2 NumPy数组基础https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb
2.3 NumPy数组的计算：通用函数https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb
https://github.com/fengdu1127/tmp#深入研究--numpy--第46天
2.4 聚合：最小值、最大值和其他值https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.04-Computation-on-arrays-aggregates.ipynb
2.5 数组的计算：广播https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.05-Computation-on-arrays-broadcasting.ipynb
2.6 比较、掩码和布尔运算https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.06-Boolean-Arrays-and-Masks.ipynb
https://github.com/fengdu1127/tmp#深入研究--numpy--第47天
2.7 花哨的索引https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.07-Fancy-Indexing.ipynb
2.8 数组的排序https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.08-Sorting.ipynb
2.9 结构化数据：NumPy的结构化数组https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.09-%3Cbr%3EStructured-Data-NumPy.ipynb
https://github.com/fengdu1127/tmp#深入研究--pandas--第48天
3 Pandas数据处理https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.00-Introduction-to-Pandas.ipynb
3.1 Pandas对象简介https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.01-Introducing-Pandas-Objects.ipynb
3.2 数据取值与选择https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.02-Data-Indexing-and-Selection.ipynb
3.3 Pandas数值运算方法https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.03-Operations-in-Pandas.ipynb
3.4 处理缺失值https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.04-Missing-Values.ipynb
3.5 层级索引https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.05-Hierarchical-Indexing.ipynb
3.6 合并数据集：ConCat和Append方法https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.06-Concat-And-Append.ipynb
https://github.com/fengdu1127/tmp#深入研究--pandas--第49天
3.7 合并数据集：合并与连接https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.07-Merge-and-Join.ipynb
3.8 累计与分组https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.08-Aggregation-and-Grouping.ipynb
3.9 数据透视表https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.09-Pivot-Tables.ipynb
https://github.com/fengdu1127/tmp#深入研究--pandas--第50天
3.10 向量化字符串操作https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.10-Working-With-Strings.ipynb
3.11 处理时间序列https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.11-Working-with-Time-Series.ipynb
3.12 高性能Pandas：eval()与query()https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.12-Performance-Eval-and-Query.ipynb
https://github.com/fengdu1127/tmp#深入研究--matplotlib--第51天
4 Matplotlib数据可视化https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb
4.1 简易线形图https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.01-Simple-Line-Plots.ipynb
4.2 简易散点图https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.02-Simple-Scatter-Plots.ipynb
4.3 可视化异常处理https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.03-Errorbars.ipynb
4.4 密度图与等高线图https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.04-Density-and-Contour-Plots.ipynb
https://github.com/fengdu1127/tmp#深入研究--matplotlib--第52天
4.5 直方图https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.05-Histograms-and-Binnings.ipynb
4.6 配置图例https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.06-Customizing-Legends.ipynb
4.7 配置颜色条https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.07-Customizing-Colorbars.ipynb
4.8 多子图https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.08-Multiple-Subplots.ipynb
4.9 文字与注释https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.09-Text-and-Annotation.ipynb
https://github.com/fengdu1127/tmp#深入研究--matplotlib--第53天
4.12 画三维图https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.12-Three-Dimensional-Plotting.ipynb
https://github.com/fengdu1127/tmp#层次聚类--第54天
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https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%2054.jpg
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