机器学习1802课程【10天】

day1 [380.74M]

demo [35.23K]

.idea [23.40K]

dictionaries [0.09K]

thinkpad.xml [0.09K]

inspectionprofiles

demo.iml [0.46K]

misc.xml [0.22K]

modules.xml [0.25K]

workspace.xml [22.38K]

kdtree.py [3.99K]

knn.py [1.76K]

s.py [0.16K]

sk_learn_knn0.py [5.77K]

work [0.16K]

video [369.65M]

01机器学习入门.wmv [67.08M]

02机器学习术语.wmv [51.04M]

03评估方法.wmv [28.35M]

04性能度量.wmv [29.59M]

05knn原理.wmv [25.65M]

06knn的实现.wmv [59.11M]

07kd树.wmv [49.17M]

08球树.wmv [13.17M]

09sklearn的实现.wmv [46.48M]

day10 [9.55M]

.ipynb_checkpoints [1.91M]

机器学习分类综合案例-checkpoint.ipynb [1.91M]

adult.data [3.79M]

adult.names [5.11K]

adult.test [1.91M]

xgboost_test.py [5.79K]

xgboost参数.docx [20.87K]

机器学习分类综合案例.ipynb [1.91M]

01ai入门.pptx [1.09M]

02k近邻.pptx [404.27K]

kdtree.py [3.97K]

knn.py [1.65K]

sk_learn_knn0.py [5.67K]

day2 [37.25M]

video [36.53M]

01线性回归.wmv [36.53M]

03线性回归.pptx [731.37K]

batch_gradient_descent.py [1.90K]

gradient_descent.py [1.37K]

gradient_descent0.py [1.18K]

leaner_regression_t1.py [0.73K]

linear_regression_simple.py [0.48K]

mini-batch_gradient_descent.py [1.97K]

sklearn_linearregression.py [1.06K]

stochastic_gradient_descent.py [2.09K]

day3 [355.83M]

c4.5_1.wmv [53.42M]

id3决策树-1.wmv [74.30M]

id3决策树-2.wmv [63.24M]

机器学习day3_笔记.docx [19.25K]

机器学习课程day3.pdf [2.66M]

决策树入门.wmv [58.99M]

逻辑回归.wmv [85.78M]

信息量入门1.wmv [17.42M]

day4 [1.39M]

.idea [18.61K]

inspectionprofiles

day4.iml [0.46K]

misc.xml [0.27K]

modules.xml [0.25K]

workspace.xml [17.63K]

.ipynb_checkpoints [9.68K]

chaid-checkpoint.ipynb [1.99K]

classification metrics-checkpoint.ipynb [7.68K]

aaa.png [14.68K]

chaid.ipynb [1.99K]

classification metrics.ipynb [7.68K]

data.csv [859.60K]

ddd.csv [504.41K]

tree.py [8.44K]

day5 [374.46M]

0727 [374.46M]

c4.5树.wmv [80.23M]

cart树.wmv [76.96M]

chaid决策树.wmv [25.72M]

day5笔记.docx [13.92K]

k-means.wmv [76.83M]

k-means教学视频.flv [6.89M]

个案相似性的计算.wmv [60.64M]

机器学习课程day5.pdf [1.97M]

决策树总结.wmv [45.21M]

day6 [541.14M]

codes_day6 [404.46K]

.idea [20.52K]

inspectionprofiles

codes_day6.iml [0.46K]

misc.xml [0.27K]

modules.xml [0.27K]

workspace.xml [19.53K]

car.csv [87.85K]

car.png

car.py [4.22K]

car1.py [3.34K]

classification metrics.ipynb [7.68K]

dbscan.py [1.04K]

get_data.ipynb [260.14K]

k-means.py [4.92K]

minibatchkmeans.py [4.68K]

preprocessing.ipynb [10.08K]

分类算法案例.wmv [192.03M]

机器学习课程day3-10_v1.pptx [8.92M]

聚类1.wmv [103.23M]

聚类2.wmv [86.45M]

聚类python1.wmv [150.11M]

day7 [2.38G]

8.1 [1.15G]

boostedtree.pdf [1.34M]

day_7笔记.docx [21.30K]

gbdt初步.docx [71.99K]

gbdt和xgboost网址.txt [0.06K]

rec007.avi [555.49M]

rec008.avi [182.96M]

rec009.avi [180.19M]

rec010.avi [252.65M]

rf,gbdt,xgboost比较.docx [17.85K]

机器学习课程day7.pdf [3.65M]

学习笔记0403.docx [528.89K]

gbdt初步.docx [71.99K]

rec001.avi [619.84M]

rec003.avi [188.29M]

rec004.avi [182.21M]

rec005.avi [264.52M]

rf,gbdt,xgboost比较.docx [17.85K]

机器学习课程day5.pptx [1.97M]

机器学习课程day7.pdf [3.65M]

day8 [1.85M]

.idea [24.46K]

inspectionprofiles

day8.iml [0.46K]

misc.xml [0.27K]

modules.xml [0.25K]

workspace.xml [23.49K]

ensemble [11.93K]

adaboost.py [0.27K]

bagging.py [0.04K]

ensemble_test.py [2.17K]

gbdt.py [4.50K]

random_forest.py [4.95K]

新建文件夹 [994.76K]

.ipynb_checkpoints [12.21K]

preprocessing-checkpoint.ipynb [12.21K]

classification metrics.ipynb [7.68K]

data.csv [859.59K]

feature selection.ipynb [5.23K]

joblib持久化存储.ipynb [6.96K]

kmeans异常值检测.ipynb [51.71K]

pipeline_一体化与处理过程.ipynb [12.30K]

preprocessing.ipynb [12.74K]

二值化标签特征.ipynb [7.17K]

交叉验证.ipynb [1.82K]

使用pipeline.ipynb [7.51K]

用k均值聚类量化图像.ipynb [9.84K]

data.csv [859.59K]

logistic_reg.py [3.13K]

day9 [1.15G]

day9_笔记.docx [21.52K]

pca意义.docx [355.77K]

rec012.avi [407.77M]

rec013.avi [143.74M]

rec014.avi [225.97M]

rec015.avi [260.04M]

rec016.avi [127.17M]

参考资料.txt [0.18K]

核函数的作用.wmv [4.72M]

机器学习课程day9.pdf [2.79M]

线性不可分可视化.wmv [5.58M]