七月在线·Kaggle课程 百度网盘(24.00G)

Kaggle实战班[24.00G]

july七月Kaggle[16.45G]

1.管窥算法.zip[1.58G]

10.概率分治和机器学习.mp4[1.45G]

2.字符串.zip[1.54G]

3.数组.zip[1.80G]

4.树.mp4[1.38G]

5.链表递归栈.mp4[1.46G]

6.查找排序.mp4[1.66G]

7.图论（上）.mp4[1.53G]

8.图论下.mp4[1.47G]

9.贪心法和动态规划.mp4[2.59G]

julyedu.com[0.00K]

七月kaggle[7.55G]

代码[960.55M]

lecture01[55.20M]

Feature_engineering_and_model_tuning[46.77M]

Feature-engineering_and_Parameter_Tuning_XGBoost[41.84M]

.ipynb_checkpoints[283.69K]

FeatureEngineering-checkpoint.ipynb[111.58K]

XGBoostmodelstuning-checkpoint.ipynb[172.11K]

FeatureEngineering.ipynb[111.58K]

Test.csv[5.10M]

test_modified.csv[7.19M]

Train.csv[12.09M]

train_modified.csv[16.91M]

XGBoostmodelstuning.ipynb[172.11K]

Kaggle_Titanic[1.05M]

.ipynb_checkpoints[494.84K]

Titanic-checkpoint.ipynb[494.84K]

test.csv[27.96K]

Titanic.ipynb[494.84K]

train.csv[59.76K]

Kaggle-Bicycle-Example[3.88M]

.ipynb_checkpoints[1.21M]

Kaggle_Bicycle_Example-checkpoint.ipynb[1.21M]

Kaggle_Bicycle_Example_files[861.49K]

Kaggle_Bicycle_Example_34_0.png[17.35K]

Kaggle_Bicycle_Example_42_0.png[14.64K]

Kaggle_Bicycle_Example_43_0.png[18.97K]

Kaggle_Bicycle_Example_44_0.png[13.57K]

Kaggle_Bicycle_Example_45_0.png[28.76K]

Kaggle_Bicycle_Example_46_1.png[145.61K]

Kaggle_Bicycle_Example_47_1.png[616.86K]

Kaggle_Bicycle_Example_49_1.png[5.73K]

Kaggle_Bicycle_Example.ipynb[1.21M]

kaggle_bike_competition_train.csv[633.16K]

blending.py[3.64K]

cs228-python-tutorial.ipynb[119.38K]

Feature_engineering_and_model_tuning.zip[8.31M]

lecture02[16.61M]

houseprice[2.05M]

input[921.89K]

sample_submission.csv[31.19K]

test.csv[440.83K]

train.csv[449.88K]

notebook[1.14M]

.ipynb_checkpoints[218.94K]

house_price_advanced-checkpoint.ipynb[132.29K]

house_price-checkpoint.ipynb[86.64K]

house_price.html[338.28K]

house_price.ipynb[86.64K]

house_price_advanced.html[389.10K]

house_price_advanced.ipynb[132.29K]

data_description.txt[13.06K]

newsstock[14.56M]

input[14.19M]

Combined_News_DJIA.csv[5.36M]

DJIA_table.csv[163.17K]

RedditNews.csv[8.68M]

notebook[372.43K]

.ipynb_checkpoints[38.39K]

news_stock-checkpoint.ipynb[38.39K]

news_stock.html[295.65K]

news_stock.ipynb[38.39K]

lecture03[3.97M]

avazu-CTR-Prediction-LR.zip[3.04M]

feature.search[0.99K]

feature.search_ads[0.71K]

feature_map.search_ads[1.23K]

generate_train_feature_mapper.py[5.70K]

generate_train_feature_reducer.py[1.25K]

kaggle-avazu-rank1.zip[260.56K]

kaggle-avazu-rank2.zip[205.69K]

search_ads_feature.sample[51.09K]

search_click_data.sample[240.40K]

Spark-Criteo-CTR-Prediction.ipynb[181.64K]

xgb_ads.conf[1.32K]

lecture04[2.03M]

notebook[2.03M]

.ipynb_checkpoints[119.85K]

searchrelevance_advanced-checkpoint.ipynb[74.94K]

searchrelevance-checkpoint.ipynb[44.90K]

news_stock.html[295.65K]

news_stock_advanced.html[893.78K]

searchrelevance.ipynb[44.90K]

searchrelevance_advanced.ipynb[75.27K]

search+relevance.html[303.88K]

search+relevance_advanced.html[348.08K]

lecture05[11.14M]

energy_forecasting_notebooks.zip[9.36M]

subway_prediction_notebook.zip[1.78M]

lecture06[825.27M]

img[103.12K]

chi_square.png[15.25K]

RGBHistogram.jpg[87.86K]

猫狗的数据[814.93M]

cats-vs-dogs.txt[0.37K]

sample_submission.csv[111.23K]

test.zip[271.30M]

train.zip[543.52M]

cat_dog.html[303.81K]

char_rnn.html[268.50K]

image_search.html[262.21K]

Kaggle第06课：走起~深度学习.pdf[4.51M]

Kaggle第06课：走起~深度学习.pptx[3.78M]

news_stock_advanced.html[893.78K]

word_rnn.html[280.51K]

lecture07[7.09M]

data.zip[6.98M]

Kaggleeventrecommendationcompetition.ipynb[40.36K]

kaggle-event-recommendation-rank1.zip[21.08K]

Rossmann_Store_Sales_competition.ipynb[50.71K]

lecture08[39.24M]

PPD_RiskControl_Competition.zip[39.24M]

课件[53.65M]

lecture01[5.94M]

Kaggle第01课：机器学习算法、工具与流程概述.pdf[5.94M]

的链接.txt[0.31K]

lecture02[5.66M]

Kaggle第02课：经济金融相关问题.pdf[5.66M]

lecture03[11.97M]

kaggle-2014-criteo.pdf[133.41K]

kaggle-avazu.pdf[180.77K]

predicting-clicks-facebook.pdf[773.88K]

阿里妈妈：大数据下的广告排序技术及实践.pdf[843.04K]

百度凤巢：DNN在凤巢CTR预估中的应用.pdf[1.26M]

从FM到FFM.pdf[982.73K]

第3课–排序与CTR预估.pdf[2.18M]

京东电商广告和推荐系统的机器学习系统实践.pdf[4.10M]

腾讯广点通：效果广告中的机器学习技术.pdf[1.59M]

lecture04[1.77M]

Kaggle第四课.pdf[1.77M]

lecture05[8.27M]

第5课：能源预测与分配问题.pdf[8.27M]

lecture07[1.13M]

第7课：推荐与销量预测相关问题.pdf[1.13M]

lecture08[2.37M]

第8课：金融风控问题.pdf[1.56M]

金融风控大赛解决方案.pdf[824.87K]

Kaggle第05课：能源预测与分配问题.pdf[8.27M]

Kaggle第06课：走起~深度学习.pdf[4.51M]

Kaggle第06课：走起~深度学习.pptx[3.78M]

1.机器学习解决问题综述课.mp4[2.16G]

第03课_kaggle案例实战班.mp4[576.15M]

第04课_kaggle案例实战班.mp4[575.23M]

第05课_kaggle案例实战班.mp4[742.62M]

第06课_kaggle案例实战班.mp4[188.91M]

第07课_kaggle案例实战班.mp4[521.82M]

第08课_kaggle案例实战班.mp4[331.53M]

第二节.mp4[1.53G]