七月在线·Kaggle实战班课程

📁 Kaggle实战班
📁 七月kaggle
📁 july七月Kaggle
📁 课件
📁 代码
📄 第04课_kaggle案例实战班【】.mp4
📄 第05课_kaggle案例实战班【】.mp4
📄 第08课_kaggle案例实战班【】.mp4
📄 第07课_kaggle案例实战班【】.mp4
📄 第06课_kaggle案例实战班【】.mp4
📄 第03课_kaggle案例实战班【】.mp4
📄 1.机器学习解决问题综述课【】.mp4
📄 9.贪心法和动态规划【】.mp4
📄 julyedu【】.com 解压密码
📄 10.概率分治和机器学习【】.mp4
📁 lecture07
📁 lecture03
📁 lecture08
📁 lecture06
📁 lecture02
📁 lecture01
📁 lecture04
📁 lecture05
📁 lecture01
📁 lecture07
📁 lecture03
📁 lecture08
📁 lecture04
📁 lecture05
📁 lecture02
📄 Kaggle第06课：走起~深度学习【】.pptx
📄 Kaggle第05课：能源预测与分配问题【】.pdf
📄 Kaggle第06课：走起~深度学习【】.pdf
📄 Rossmann_Store_Sales_competition【】.ipynb
📄 data【】.zip
📄 Kaggle event recommendation competition【】.ipynb
📄 kaggle-event-recommendation-rank1【】.zip
📄 PPD_RiskControl_Competition【】.zip
📄 search_ads_feature【】.sample
📄 search_click_data【】.sample
📄 feature_map【】.search_ads
📄 feature【】.search_ads
📄 avazu-CTR-Prediction-LR【】.zip
📄 kaggle-avazu-rank1【】.zip
📄 kaggle-avazu-rank2【】.zip
📄 xgb_ads【】.conf
📄 feature【】.search
📄 generate_train_feature_reducer【】.py
📄 generate_train_feature_mapper【】.py
📄 Spark-Criteo-CTR-Prediction【】.ipynb
📁 猫狗的数据
📁 img
📄 cat_dog【】.html
📄 Kaggle第06课：走起~深度学习【】.pdf
📄 image_search【】.html
📄 char_rnn【】.html
📄 word_rnn【】.html
📄 Kaggle第06课：走起~深度学习【】.pptx
📄 news_stock_advanced【】.html
📄 energy_forecasting_notebooks【】.zip
📄 subway_prediction_notebook【】.zip
📁 input数据太大。就不传了。自己下载吧~ - 老师留
📁 notebook
📁 Feature_engineering_and_model_tuning
📄 blending【】.py
📄 Feature_engineering_and_model_tuning【】.zip
📄 cs228-python-tutorial【】.ipynb
📁 news stock
📁 house price
📄 第8课：金融风控问题【】.pdf
📄 金融风控大赛解决方案【】.pdf
📄 Kaggle第01课：机器学习算法、工具与流程概述【】.pdf
📄 分享的链接【】.txt
📄 kaggle-2014-criteo【】.pdf
📄 predicting-clicks-facebook【】.pdf
📄 百度凤巢：DNN在凤巢CTR预估中的应用【】.pdf
📄 腾讯广点通：效果广告中的机器学习技术【】.pdf
📄 第3课--排序与CTR预估【】.pdf
📄 kaggle-avazu【】.pdf
📄 从FM到FFM【】.pdf
📄 阿里妈妈：大数据下的广告排序技术及实践【】.pdf
📄 京东电商广告和推荐系统的机器学习系统实践【】.pdf
📄 第7课：推荐与销量预测相关问题【】.pdf
📄 cats-vs-dogs【】.txt
📄 train【】.zip
📄 test【】.zip
📄 sample_submission【】.csv
📄 第5课：能源预测与分配问题【】.pdf
📄 Kaggle第四课【】.pdf
📄 Kaggle第02课：经济金融相关问题【】.pdf
📁 Kaggle-Bicycle-Example
📁 Kaggle_Titanic
📁 Feature-engineering_and_Parameter_Tuning_XGBoost
📄 chi_square【】.png
📄 RGBHistogram【】.jpg
📁 .ipynb_checkpoints
📄 search relevance【】.ipynb
📄 search relevance_advanced【】.ipynb
📄 news_stock【】.html
📄 news_stock_advanced【】.html
📄 search+relevance_advanced【】.html
📄 search+relevance【】.html
📁 input
📁 _ipynb_checkpoints
📁 notebook
📁 .ipynb_checkpoints
📄 test【】.csv
📄 train【】.csv
📄 Titanic【】.ipynb
📁 notebook
📁 input
📁 _ipynb_checkpoints
📄 data_description【】.txt
📁 .ipynb_checkpoints
📁 Kaggle_Bicycle_Example_files
📄 kaggle_bike_competition_train【】.csv
📄 Kaggle_Bicycle_Example【】.ipynb
📄 search relevance_advanced-checkpoint【】.ipynb
📄 search relevance-checkpoint【】.ipynb
📄 RedditNews【】.csv
📄 DJIA_table【】.csv
📄 Combined_News_DJIA【】.csv
📁 .ipynb_checkpoints
📄 Test【】.csv
📄 test_modified【】.csv
📄 train_modified【】.csv
📄 XGBoost models tuning【】.ipynb
📄 Train【】.csv
📄 Feature Engineering【】.ipynb
📄 test【】.csv
📄 sample_submission【】.csv
📄 train【】.csv
📁 .ipynb_checkpoints
📄 news_stock【】.html
📄 news_stock【】.ipynb
📁 .ipynb_checkpoints
📄 house_price_advanced【】.html
📄 house_price【】.html
📄 house_price_advanced【】.ipynb
📄 house_price【】.ipynb
📄 Titanic-checkpoint【】.ipynb
📄 Kaggle_Bicycle_Example-checkpoint【】.ipynb
📄 Kaggle_Bicycle_Example_46_1【】.png
📄 Kaggle_Bicycle_Example_44_0【】.png
📄 Kaggle_Bicycle_Example_34_0【】.png
📄 Kaggle_Bicycle_Example_47_1【】.png
📄 Kaggle_Bicycle_Example_43_0【】.png
📄 Kaggle_Bicycle_Example_42_0【】.png
📄 Kaggle_Bicycle_Example_49_1【】.png
📄 Kaggle_Bicycle_Example_45_0【】.png
📄 XGBoost models tuning-checkpoint【】.ipynb
📄 Feature Engineering-checkpoint【】.ipynb
📄 news_stock-checkpoint【】.ipynb
📄 house_price_advanced-checkpoint【】.ipynb
📄 house_price-checkpoint【】.ipynb