文件名称：python机器学习案例
文件大小：6.97MB
文件格式：RAR
更新时间：2021-11-03 06:30:49
机器学习
机器学习的学习小案例，包含logistic-regression、k-mens、randomforest等
【文件预览】：
python机器学习案例
----machineLearning()
--------ml_7_mulabel.ipynb(11KB)
--------.ipynb_checkpoints()
--------cleaned_loans_2007.csv(4.53MB)
--------ml_GradientDescent.ipynb(213KB)
--------decision tree 1.8.ipynb(8KB)
--------admissions.csv(25KB)
--------ml_6_clustering.ipynb(25KB)
--------ml_4_Cross-validation.ipynb(16KB)
--------iris.csv(5KB)
--------ml_buildDecisionTree.ipynb(4KB)
--------pga.csv(2KB)
--------nba_2013.csv(71KB)
--------auto-mpg.data(30KB)
--------Untitled.ipynb(72B)
--------114_congress.csv(4KB)
--------ml_1_introduce.ipynb(87KB)
--------ml_loanProject.ipynb(31KB)
--------ml_9_KMEANS.ipynb(7KB)
--------ml_8_overfit.ipynb(22KB)
--------ml_neuralnetwork.ipynb(96KB)
--------loans_2007.csv(14.79MB)
--------ml_randomForest.ipynb(4KB)
--------filtered_loans_2007.csv(6.48MB)
--------ml_decisionTree.ipynb(18KB)
--------ml_2_logistic-regression.ipynb(64KB)
--------ml_DTandRandmoF_scikieLearn.ipynb(16KB)
--------ml_9_k-means.ipynb(514B)
--------ml_3_logisticRes.ipynb(5KB)
--------ml_kmeans_nba.ipynb(112KB)
--------ml_5_kcross.ipynb(7KB)
--------cleaned_loans2007.csv(4.45MB)
--------income.csv(72KB)