{"id":19083098,"url":"https://github.com/cr-mao/machine-learning","last_synced_at":"2025-11-12T05:31:15.168Z","repository":{"id":254249376,"uuid":"845934869","full_name":"cr-mao/machine-learning","owner":"cr-mao","description":"机器学习笔记","archived":false,"fork":false,"pushed_at":"2024-09-21T11:00:44.000Z","size":29844,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-22T06:25:21.375Z","etag":null,"topics":["data-analysis","data-handling","machine-learning","math","numpy","pandas"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cr-mao.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-22T08:05:34.000Z","updated_at":"2024-09-21T11:00:48.000Z","dependencies_parsed_at":"2025-02-22T06:35:01.713Z","dependency_job_id":null,"html_url":"https://github.com/cr-mao/machine-learning","commit_stats":null,"previous_names":["cr-mao/learn-ai","cr-mao/machine-learning"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/cr-mao/machine-learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cr-mao%2Fmachine-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cr-mao%2Fmachine-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cr-mao%2Fmachine-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cr-mao%2Fmachine-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cr-mao","download_url":"https://codeload.github.com/cr-mao/machine-learning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cr-mao%2Fmachine-learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":283980920,"owners_count":26927175,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-11-12T02:00:06.336Z","response_time":59,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-handling","machine-learning","math","numpy","pandas"],"created_at":"2024-11-09T02:46:01.570Z","updated_at":"2025-11-12T05:31:15.154Z","avatar_url":"https://github.com/cr-mao.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# machine learning\n\n数据分析、挖掘， 机器学习等学习笔记\n\n- 数学基础\n- 数据分析与可视化\n- 机器学习\n- python\n\n## 数学基础\n\n- [高等数学](math/高等数学.md)\n- [线性代数](math/线性代数.md)\n- [统计学](math/统计学.md)\n\n## 数据处理分析与可视化\n\njupyter notebook ,numpy,pandas,matplotlib\n\n- [开发环境](datahandling/开发环境.md)\n- [数据领域中的专业术语](datahandling/数据领域中的专业术语.md)\n- [numpy数据基础](datahandling/01-NumpyArrayBasics/01-NumpyArrayBasics.ipynb)\n- [numpy数组创建](datahandling/02-NumpyCreateArray/02CreateNumpyArray.ipynb)\n- [numpy数组基本操作](datahandling/03-NumpyArrayBasicOperations/03-NumpyArrayBasicOperations.ipynb)\n    - 数组的访问、切片、bool索引、条件索引\n- [numpy数组运算](datahandling/04-NumpyComputationArray/04-ComputationNumpyArray.ipynb)\n    - 数组加减乘除、矩阵运算、矩阵的逆、伪逆、矩阵转置、数组升维\n- [numpy数据合并和拆分](datahandling/05-NumpyConcatenateAndSplit/05-ConcatenateAndSplit.ipynb)\n- [numpy统计运算](datahandling/06-NumpyAggregationOperator/06-AggregationOperator.ipynb)\n- [numpy排序找索引操作](datahandling/07-NumpyArgAndSortOperation/07-ArgAndSortOperation.ipynb)\n- [numpy比较和神奇索引](datahandling/08-ComparisonAndFancyIndexing/08-ComparisonAndFancyIndexing.ipynb)\n- [pandas中的数据结构](datahandling/20-PandasDataFrameSeriesPanel/pandasDataFrameSeriesPanel.ipynb)\n- [Series创建、属性、计算](datahandling/21-SeriesBasic/seriesBasic.ipynb)\n- [Series的索引与基本操作](datahandling/22-SerieIndexAndOperation/22-seriesIndexAndOperation.ipynb)\n- pandas\n    - [dataframe创建、基本属性与索引切片](datahandling/23-PandasDataframeBasic/dataframeBasic.ipynb)\n    - [dataframe中的方法与索引技巧](datahandling/24-PandasDataframeMethodAndIndex/dataframeMethodAndIndex.ipynb)\n    - [dataframe统计运算和逻辑运算](datahandling/25-PandasDataframeStatAndLogic/dataframeStatAndLogic.ipynb)\n    - [dataframe数据计算](datahandling/26-PandasDataframeCompute/dataframe_compute.ipynb)\n    - [时间序列](datahandling/27-PandasTime/pandas_time.ipynb)\n    - [io的读取和存储、缺失值处理、离散化处理](datahandling/28-PandasIoAndNanAndDiscrete/pandasIoNan.ipynb)\n- matplot\n    - [matplot基础](datahandling/31-Matplotlib-Basics/Matplotlib-Basics.ipynb)\n    - [matplot其他](datahandling/32-Matplot/matplot.ipynb)\n\n## 机器学习\n\n- knn\n    - [knn理论、公式](machinelearning/knn.md)\n    - [实现自己的knn](machinelearning/knn/01-kNNBasics/kNNBasics.ipynb)\n    - [sklearn中的knn](machinelearning/knn/02-kNNInScikitLearn/kNNinScikitlearn.ipynb)\n    - [训练数据集和测试数据集拆分](machinelearning/knn/03-TrainTestSplit/TrainTestSplit.ipynb)\n    - [结果准确度](machinelearning/knn/04-AccuracyScore/AccuracyScore.ipynb)\n    - [超参数寻找](machinelearning/knn/05-HyperParameters/HyperParameters.ipynb)\n    - [网格搜索超参数](machinelearning/knn/06-GridSearch/GridSearch.ipynb)\n    - [数据归一化和标准化](machinelearning/knn/07-FeatureScaling/FeatureScaling.ipynb)\n    - [sklearn中的标准化](machinelearning/knn/08-ScalerinScikitLearn/ScalerInScikitLearn.ipyn)\n- 线性回归法\n    - [线性回归理论、公式](machinelearning/线性回归.md)\n    - [简单线性回归实现](machinelearning/linearRegression/01-SimpleLinearRegressionImplementation/SimpleLinearRegressionImplementation.ipynb)\n    - [向量化运算效率高](machinelearning/linearRegression/02-Vectorization/Vectorization.ipynb)\n    - [衡量回归算法的标准，MSE、MAE](machinelearning/linearRegression/03-RegressionMetricsMSE-vs-MAE/RegressionMetricsMSE-vs-MAE.ipynb)\n    - [最好的衡量线性回归法的指标：R Squared ](machinelearning/linearRegression/04-R-Squared/R-Squared.ipynb)\n    - [正规方程法实现多元线性回归](machinelearning/linearRegression/05-OurLinearRegression/OurLinearRegression.ipynb)\n    - [sklearn中解决线性回归](machinelearning/linearRegression/06-RegressionInScikitLlearn/RegressionInScikitlearn.ipynb)\n    - [模拟欠拟合与过拟合、正则化处理](machinelearning/linearRegression/08-UnderfittingAndOverfitting/underfittingAndOverfitting.ipynb)\n\n- 梯度下降法\n    - [梯度下降法理论、公式](machinelearning/梯度下降法.md)\n    - [模拟实现梯度下降法(单变量)](machinelearning/gradientDescent/01-GradientDescentSimulations/01-GradientDescentSimulations.ipynb)\n    - [在线性回归中实现梯度下降法](machinelearning/gradientDescent/02-ImplementGradientDescentInLinearRegression/02-ImplementGradientDescentInLinearRegression.ipynb)\n    - [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)\n    - [随机梯度下降法](machinelearning/gradientDescent/04-StochasticGradientDescent/04-StochasticGradientDescent.ipynb)\n    - [sklearn中的随机梯度下降法](machinelearning/gradientDescent/05-SGDInScikitLearn/SGDInScikitLearn.ipynb)\n    - [关于梯度的计算调试](machinelearning/gradientDescent/06-DebugGradient/DebugGradient.ipynb)\n- 多项式回归与模型泛化\n  - [什么是多项式回归](machinelearning/polynomialRegressionAndModelGeneralization/01-whatIsPolynomialRegression/whatIsPolynomialRegression.ipynb)\n  - [scikit-learn中的多项式回归和Pipeline](machinelearning/polynomialRegressionAndModelGeneralization/02-PolynomialRegressionInScikitLearn/polynomialRegressionInScikitLearn.ipynb)\n  - [过拟合与欠拟合](machinelearning/polynomialRegressionAndModelGeneralization/03-OverfittingAndUnderfitting/overfittingAndUnderfitting.ipynb)\n  - [为什么使用测试数据集](machinelearning/polynomialRegressionAndModelGeneralization/04-WhyTrainTestSplit/WhyTrainTestSplit.ipynb)\n  - [学习曲线](machinelearning/polynomialRegressionAndModelGeneralization/05-LearningCurve/LearningCurve.ipynb)\n  - [k折交叉验证](machinelearning/polynomialRegressionAndModelGeneralization/06-ValidationAndCrossValidation/validationAndCrossValidation.ipynb)\n  - [岭回归](machinelearning/polynomialRegressionAndModelGeneralization/08-ModelRegularizationAndRidgeRegression/modelRegularizationAndRidgeRegression.ipynb)\n  - [LASSO回归](machinelearning/polynomialRegressionAndModelGeneralization/09-LASSORegression/LASSO-Regression.ipynb)\n- PCA\n  - [PCA理论、公式](machinelearning/PCA与梯度上升法.md)\n  - [使用梯度上升法实现PCA](machinelearning/pcaAndGradientAscent/01-Implement-PCA-in-BGA/Implement-PCA-in-BGA.ipynb)\n  \n- 逻辑回归\n    - [逻辑回归理论、公式](machinelearning/逻辑回归.md)\n    - [sigmod函数](machinelearning/logisticRegression/01-WhatIsLogisticRegression/01-What-is-Logistic-Regression.ipynb)\n    - [实现逻辑回归](machinelearning/logisticRegression/02-ImplementLogisticRegression/implementLogisticRegression.ipynb)\n    - [决策边界](machinelearning/logisticRegression/03-DecisionBoundary/Decision-Boundary.ipynb)\n    - [添加多项式](machinelearning/logisticRegression/04-PolynomialFeaturesInLogisticRegression/polynomialFeaturesInLogisticRegression.ipynb)\n    - [scikit-learn中的逻辑回归](machinelearning/logisticRegression/05-logisticRegressionInScikitLearn/logisticRegressionInScikitLearn.ipynb)\n    - [解决多分类问题](machinelearning/logisticRegression/06-OvrAndOvo/ovrAndOvo.ipynb)\n- 评价分类结果 \n  - [实现混淆矩阵，精准率和召回率](machinelearning/classificationPerformanceMeasures/01-implementConfusionMatrixPrecisionAndRecall/Implement-Confusion-Matrix-Precision-and-Recall.ipynb)\n  - [F1 score](machinelearning/classificationPerformanceMeasures/02-F1Score/F1Score.ipynb)\n  - [精准度和召回率的平衡](machinelearning/classificationPerformanceMeasures/03-PrecisionRecallTradeoff/precisionRecallTradeoff.ipynb)\n  - [精准度-召回率曲线](machinelearning/classificationPerformanceMeasures/04-precisionRecallCurve/precisionRecallCurve.ipynb)\n  - [ROC曲线与AUC指标](machinelearning/classificationPerformanceMeasures/05-rocCurve/rocCurve.ipynb)\n  - [多分类看预测结果](machinelearning/classificationPerformanceMeasures/06-confusionMatrixInMulticlassClassification/confusionMatrixInMulticlassClassification.ipynb)\n- k-means\n  - [k-means理论](machinelearning/Kmeans.md)\n  - [特征降维、kmeans实践](machinelearning/kmeans/kmeans.ipynb)\n- 朴素叶贝斯\n- 神经网络\n- 推荐系统相关\n  - [推荐系统快速入门](machinelearning/推荐系统入门.md)\n  - [用户口味、余弦相似性](machinelearning/recommand/01consine_simiartiy/consine_similarty.ipynb)\n  - [用户消费能力、标准化欧式距离](machinelearning/recommand/02distance/distance.ipynb)\n  - [NearestNeighbors、余弦相似性找出最相似的用户](machinelearning/recommand/03NearestNeighborsAndConsineSimiarity/NearestNeighbors_and_consine_simiarity.ipynb)\n\n## links\n\n- 高等数学第6版上册\n- 基于Python的数据分析与可视化-掘金小册\n- 重学线性代数-极客时间\n- Python核心技术与实战-极客时间\n- 数据分析实战45讲-极客时间\n- 机器学习(公式推导与代码实现)\n- 从零开始机器学习的数学原理和算法实践\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcr-mao%2Fmachine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcr-mao%2Fmachine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcr-mao%2Fmachine-learning/lists"}