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https://github.com/cr-mao/machine-learning

机器学习笔记
https://github.com/cr-mao/machine-learning
data-analysis data-handling machine-learning math numpy pandas
Last synced: 6 days ago
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机器学习笔记
Host: GitHub
URL: https://github.com/cr-mao/machine-learning
Owner: cr-mao
Created: 2024-08-22T08:05:34.000Z (5 months ago)
Default Branch: main
Last Pushed: 2024-09-21T11:00:44.000Z (4 months ago)
Last Synced: 2024-11-09T02:45:53.866Z (2 months ago)
Topics: data-analysis, data-handling, machine-learning, math, numpy, pandas
Language: Jupyter Notebook
Homepage:
Size: 28.5 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

README

        # machine learning

数据分析、挖掘， 机器学习等学习笔记

- 数学基础

- 数据分析与可视化

- 机器学习

- python

## 数学基础

- [高等数学](math/高等数学.md)

- [线性代数](math/线性代数.md)

- [统计学](math/统计学.md)

## 数据处理分析与可视化

jupyter notebook ,numpy,pandas,matplotlib

- [开发环境](datahandling/开发环境.md)

- [数据领域中的专业术语](datahandling/数据领域中的专业术语.md)

- [numpy数据基础](datahandling/01-NumpyArrayBasics/01-NumpyArrayBasics.ipynb)

- [numpy数组创建](datahandling/02-NumpyCreateArray/02CreateNumpyArray.ipynb)

- [numpy数组基本操作](datahandling/03-NumpyArrayBasicOperations/03-NumpyArrayBasicOperations.ipynb)

    - 数组的访问、切片、bool索引、条件索引

- [numpy数组运算](datahandling/04-NumpyComputationArray/04-ComputationNumpyArray.ipynb)

    - 数组加减乘除、矩阵运算、矩阵的逆、伪逆、矩阵转置、数组升维

- [numpy数据合并和拆分](datahandling/05-NumpyConcatenateAndSplit/05-ConcatenateAndSplit.ipynb)

- [numpy统计运算](datahandling/06-NumpyAggregationOperator/06-AggregationOperator.ipynb)

- [numpy排序找索引操作](datahandling/07-NumpyArgAndSortOperation/07-ArgAndSortOperation.ipynb)

- [numpy比较和神奇索引](datahandling/08-ComparisonAndFancyIndexing/08-ComparisonAndFancyIndexing.ipynb)

- [pandas中的数据结构](datahandling/20-PandasDataFrameSeriesPanel/pandasDataFrameSeriesPanel.ipynb)

- [Series创建、属性、计算](datahandling/21-SeriesBasic/seriesBasic.ipynb)

- [Series的索引与基本操作](datahandling/22-SerieIndexAndOperation/22-seriesIndexAndOperation.ipynb)

- pandas

    - [dataframe创建、基本属性与索引切片](datahandling/23-PandasDataframeBasic/dataframeBasic.ipynb)

    - [dataframe中的方法与索引技巧](datahandling/24-PandasDataframeMethodAndIndex/dataframeMethodAndIndex.ipynb)

    - [dataframe统计运算和逻辑运算](datahandling/25-PandasDataframeStatAndLogic/dataframeStatAndLogic.ipynb)

    - [dataframe数据计算](datahandling/26-PandasDataframeCompute/dataframe_compute.ipynb)

    - [时间序列](datahandling/27-PandasTime/pandas_time.ipynb)

    - [io的读取和存储、缺失值处理、离散化处理](datahandling/28-PandasIoAndNanAndDiscrete/pandasIoNan.ipynb)

- matplot

    - [matplot基础](datahandling/31-Matplotlib-Basics/Matplotlib-Basics.ipynb)

    - [matplot其他](datahandling/32-Matplot/matplot.ipynb)

## 机器学习

- knn

    - [knn理论、公式](machinelearning/knn.md)

    - [实现自己的knn](machinelearning/knn/01-kNNBasics/kNNBasics.ipynb)

    - [sklearn中的knn](machinelearning/knn/02-kNNInScikitLearn/kNNinScikitlearn.ipynb)

    - [训练数据集和测试数据集拆分](machinelearning/knn/03-TrainTestSplit/TrainTestSplit.ipynb)

    - [结果准确度](machinelearning/knn/04-AccuracyScore/AccuracyScore.ipynb)

    - [超参数寻找](machinelearning/knn/05-HyperParameters/HyperParameters.ipynb)

    - [网格搜索超参数](machinelearning/knn/06-GridSearch/GridSearch.ipynb)

    - [数据归一化和标准化](machinelearning/knn/07-FeatureScaling/FeatureScaling.ipynb)

    - [sklearn中的标准化](machinelearning/knn/08-ScalerinScikitLearn/ScalerInScikitLearn.ipyn)

- 线性回归法

    - [线性回归理论、公式](machinelearning/线性回归.md)

    - [简单线性回归实现](machinelearning/linearRegression/01-SimpleLinearRegressionImplementation/SimpleLinearRegressionImplementation.ipynb)

    - [向量化运算效率高](machinelearning/linearRegression/02-Vectorization/Vectorization.ipynb)

    - [衡量回归算法的标准，MSE、MAE](machinelearning/linearRegression/03-RegressionMetricsMSE-vs-MAE/RegressionMetricsMSE-vs-MAE.ipynb)

    - [最好的衡量线性回归法的指标：R Squared ](machinelearning/linearRegression/04-R-Squared/R-Squared.ipynb)

    - [正规方程法实现多元线性回归](machinelearning/linearRegression/05-OurLinearRegression/OurLinearRegression.ipynb)

    - [sklearn中解决线性回归](machinelearning/linearRegression/06-RegressionInScikitLlearn/RegressionInScikitlearn.ipynb)

    - [模拟欠拟合与过拟合、正则化处理](machinelearning/linearRegression/08-UnderfittingAndOverfitting/underfittingAndOverfitting.ipynb)

- 梯度下降法

    - [梯度下降法理论、公式](machinelearning/梯度下降法.md)

    - [模拟实现梯度下降法(单变量)](machinelearning/gradientDescent/01-GradientDescentSimulations/01-GradientDescentSimulations.ipynb)

    - [在线性回归中实现梯度下降法](machinelearning/gradientDescent/02-ImplementGradientDescentInLinearRegression/02-ImplementGradientDescentInLinearRegression.ipynb)

    - [梯度下降向量化公式及性能和正规方程对比](machinelearning/gradientDescent/03-VectorizeGradientDescent/03-VectorizeGradientDescent.ipynb)

    - [随机梯度下降法](machinelearning/gradientDescent/04-StochasticGradientDescent/04-StochasticGradientDescent.ipynb)

    - [sklearn中的随机梯度下降法](machinelearning/gradientDescent/05-SGDInScikitLearn/SGDInScikitLearn.ipynb)

    - [关于梯度的计算调试](machinelearning/gradientDescent/06-DebugGradient/DebugGradient.ipynb)

- 多项式回归与模型泛化

  - [什么是多项式回归](machinelearning/polynomialRegressionAndModelGeneralization/01-whatIsPolynomialRegression/whatIsPolynomialRegression.ipynb)

  - [scikit-learn中的多项式回归和Pipeline](machinelearning/polynomialRegressionAndModelGeneralization/02-PolynomialRegressionInScikitLearn/polynomialRegressionInScikitLearn.ipynb)

  - [过拟合与欠拟合](machinelearning/polynomialRegressionAndModelGeneralization/03-OverfittingAndUnderfitting/overfittingAndUnderfitting.ipynb)

  - [为什么使用测试数据集](machinelearning/polynomialRegressionAndModelGeneralization/04-WhyTrainTestSplit/WhyTrainTestSplit.ipynb)

  - [学习曲线](machinelearning/polynomialRegressionAndModelGeneralization/05-LearningCurve/LearningCurve.ipynb)

  - [k折交叉验证](machinelearning/polynomialRegressionAndModelGeneralization/06-ValidationAndCrossValidation/validationAndCrossValidation.ipynb)

  - [岭回归](machinelearning/polynomialRegressionAndModelGeneralization/08-ModelRegularizationAndRidgeRegression/modelRegularizationAndRidgeRegression.ipynb)

  - [LASSO回归](machinelearning/polynomialRegressionAndModelGeneralization/09-LASSORegression/LASSO-Regression.ipynb)

- PCA

  - [PCA理论、公式](machinelearning/PCA与梯度上升法.md)

  - [使用梯度上升法实现PCA](machinelearning/pcaAndGradientAscent/01-Implement-PCA-in-BGA/Implement-PCA-in-BGA.ipynb)

  

- 逻辑回归

    - [逻辑回归理论、公式](machinelearning/逻辑回归.md)

    - [sigmod函数](machinelearning/logisticRegression/01-WhatIsLogisticRegression/01-What-is-Logistic-Regression.ipynb)

    - [实现逻辑回归](machinelearning/logisticRegression/02-ImplementLogisticRegression/implementLogisticRegression.ipynb)

    - [决策边界](machinelearning/logisticRegression/03-DecisionBoundary/Decision-Boundary.ipynb)

    - [添加多项式](machinelearning/logisticRegression/04-PolynomialFeaturesInLogisticRegression/polynomialFeaturesInLogisticRegression.ipynb)

    - [scikit-learn中的逻辑回归](machinelearning/logisticRegression/05-logisticRegressionInScikitLearn/logisticRegressionInScikitLearn.ipynb)

    - [解决多分类问题](machinelearning/logisticRegression/06-OvrAndOvo/ovrAndOvo.ipynb)

- 评价分类结果 

  - [实现混淆矩阵，精准率和召回率](machinelearning/classificationPerformanceMeasures/01-implementConfusionMatrixPrecisionAndRecall/Implement-Confusion-Matrix-Precision-and-Recall.ipynb)

  - [F1 score](machinelearning/classificationPerformanceMeasures/02-F1Score/F1Score.ipynb)

  - [精准度和召回率的平衡](machinelearning/classificationPerformanceMeasures/03-PrecisionRecallTradeoff/precisionRecallTradeoff.ipynb)

  - [精准度-召回率曲线](machinelearning/classificationPerformanceMeasures/04-precisionRecallCurve/precisionRecallCurve.ipynb)

  - [ROC曲线与AUC指标](machinelearning/classificationPerformanceMeasures/05-rocCurve/rocCurve.ipynb)

  - [多分类看预测结果](machinelearning/classificationPerformanceMeasures/06-confusionMatrixInMulticlassClassification/confusionMatrixInMulticlassClassification.ipynb)

- k-means

  - [k-means理论](machinelearning/Kmeans.md)

  - [特征降维、kmeans实践](machinelearning/kmeans/kmeans.ipynb)

- 朴素叶贝斯

- 神经网络

- 推荐系统相关

  - [推荐系统快速入门](machinelearning/推荐系统入门.md)

  - [用户口味、余弦相似性](machinelearning/recommand/01consine_simiartiy/consine_similarty.ipynb)

  - [用户消费能力、标准化欧式距离](machinelearning/recommand/02distance/distance.ipynb)

  - [NearestNeighbors、余弦相似性找出最相似的用户](machinelearning/recommand/03NearestNeighborsAndConsineSimiarity/NearestNeighbors_and_consine_simiarity.ipynb)

## links

- 高等数学第6版上册

- 基于Python的数据分析与可视化-掘金小册

- 重学线性代数-极客时间

- Python核心技术与实战-极客时间

- 数据分析实战45讲-极客时间

- 机器学习(公式推导与代码实现)

- 从零开始机器学习的数学原理和算法实践