Finished in late November 2020:
It is tough today and tomorrow will be tougher.but the day after tomorrow is beautiful!
many people died in the evening of tomorrow,only the ture hero can see the rising sun on the third day!（马云英文演讲）
I hope that different paths will return together, I can tell you the path of the time
table of Contents
1. Numpy summary
numpy keyword, several basic operations, .concatenate, newaxis
https://blog.csdn.net/weixin_45316122/article/details/100531845
ML.Numpy common function sharing
https://blog.csdn.net/weixin_45316122/article/details/101456159
https://blog.csdn.net/weixin_45316122/article/details/102839006
Numpy implements linear_regeression
https://blog.csdn.net/weixin_45316122/article/details/102841123
https://blog.csdn.net/weixin_45316122/article/details/102839924
Python3 random and numpy.random difference
https://blog.csdn.net/weixin_45316122/article/details/102838174
python3 random和numpy.random（2）
https://blog.csdn.net/weixin_45316122/article/details/102780903
Comparison of pytorch's torch tensor and numpy array, and the excitation function in Torch
https://blog.csdn.net/weixin_45316122/article/details/102798965
Two. Pandas summary
ML Pandas common function sharing
https://blog.csdn.net/weixin_45316122/article/details/101457187
Detailed explanation of the parameters of pandas read_csv and to_csv methods (return)
https://blog.csdn.net/weixin_45316122/article/details/102819324
https://blog.csdn.net/weixin_45316122/article/details/102839106
Pandas Quick Reference Manual Chinese Version (Revised)
https://blog.csdn.net/weixin_45316122/article/details/105558006
Three. Matplotlib summary
Python3 data analysis matplotlib drawing notes
https://blog.csdn.net/weixin_45316122/article/details/98261649
ML Matplotlib common function sharing
https://blog.csdn.net/weixin_45316122/article/details/101456416
Matplotlib's drawing foundation
https://blog.csdn.net/weixin_45316122/article/details/102840010
Four. Seaborn summary
Common problems of python3.ML.Seaborn drawing library
https://blog.csdn.net/weixin_45316122/article/details/98784932
python3.ML.Seaborn drawing-----Style part-------Detailed explanation
https://blog.csdn.net/weixin_45316122/article/details/98535733
python3.ML.Seaborn drawing----Color part-----------Detailed explanation
https://blog.csdn.net/weixin_45316122/article/details/98535531
python3.ML.Seaborn drawing --------Var part --------------Detailed explanation
https://blog.csdn.net/weixin_45316122/article/details/98535282
python3.ML.Seaborn drawing REG-regression analysis drawing (whether tipped data case)
https://blog.csdn.net/weixin_45316122/article/details/98534957
python3.ML.Seaborn drawing category part
https://blog.csdn.net/weixin_45316122/article/details/98534652
Five. Sklearn summary
Python machine learning notes: sklearn library learning (transfer)
https://blog.csdn.net/weixin_45316122/article/details/100112345
ML.Sklearn feature engineering, algorithm, SVM, Pipeline mechanism
https://blog.csdn.net/weixin_45316122/article/details/101458757
sklearn iris two classification
https://blog.csdn.net/weixin_45316122/article/details/102853601
SKlearn study notes~~Basic part 01 General
https://blog.csdn.net/weixin_45316122/article/details/102799806
SKlearn advanced tutorial notes 02~~ Normalization, test neural network (Evaluation)
https://blog.csdn.net/weixin_45316122/article/details/102800567
SKlearn actual insurance cost forecast
https://blog.csdn.net/weixin_45316122/article/details/102839338
SKlearn loads data sets, ipython notebook shortcuts, SKlearn learning and prediction, persistence
https://blog.csdn.net/weixin_45316122/article/details/102838025
SKlearn data feature preprocessing
https://blog.csdn.net/weixin_45316122/article/details/102836263
The difference between fit(), transform() and fit_transform() in SKlearn data preprocessing
https://blog.csdn.net/weixin_45316122/article/details/102833278
https://blog.csdn.net/weixin_45316122/article/details/102800883
Python SKlearn main functions, module classification, code application
https://blog.csdn.net/weixin_45316122/article/details/102564321
Python SKlearn main functions, module classification, code application (2)
https://blog.csdn.net/weixin_45316122/article/details/102841107
6. M'L Math Summary
ML linked list stack queue induction
https://blog.csdn.net/weixin_45316122/article/details/101466012
ML Mathematics Fundamentals 12 Span, basis, subspace
https://blog.csdn.net/weixin_45316122/article/details/101418175
ML Mathematical Fundamentals 10 Maximum Likelihood Estimation
https://blog.csdn.net/weixin_45316122/article/details/101396472
ML Mathematics Fundamentals 09 sample moments, moment estimation
https://blog.csdn.net/weixin_45316122/article/details/101396271
ML Fundamentals of Mathematics 08 Large Numbers, Bernoulli, Central Limit Theorem
https://blog.csdn.net/weixin_45316122/article/details/101395882
ML Mathematical Foundation 07 Skewness and Kurtosis
https://blog.csdn.net/weixin_45316122/article/details/101395759
ML Mathematical Foundation 06 Covariance
https://blog.csdn.net/weixin_45316122/article/details/101342260
ML Mathematical Fundamentals 05 Expectation and Variance
https://blog.csdn.net/weixin_45316122/article/details/101341272
ML Mathematical Fundamentals 04 Probability Calculation and Rejection Sampling
https://blog.csdn.net/weixin_45316122/article/details/101341181
https://blog.csdn.net/weixin_45316122/article/details/101318930
ML Mathematical Foundation 02 Probability and Statistics
https://blog.csdn.net/weixin_45316122/article/details/101519978
ML Mathematical Foundation 01 Calculus
https://blog.csdn.net/weixin_45316122/article/details/101512009
Taylor Expansion and Quasi-Newton
https://blog.csdn.net/weixin_45316122/article/details/101315753
ML Mathematical Fundamentals Calculus and Gradient
https://blog.csdn.net/weixin_45316122/article/details/101314732
ML Mathematical Basic String Algorithm
https://blog.csdn.net/weixin_45316122/article/details/101480991
*************At this stage, the python data analysis of blog posts, machine learning notes have been sorted out********************* *****