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├第01课.机器学习解决问题综述课.mp4
├第03课_kaggle案例实战班.mp4
├第04课_kaggle案例实战班.mp4
├第05课_kaggle案例实战班.mp4
├第06课_kaggle案例实战班.mp4
├第07课_kaggle案例实战班.mp4
├第08课_kaggle案例实战班.mp4
├第二节.mp4
├
│├
││├blending.py
││├cs228-python-tutorial.ipynb
││├Feature_engineering_and_model_tuning.zip
││├
│││├
││││├FeatureEngineering.ipynb
││││├Test.csv
││││├test_modified.csv
││││├Train.csv
││││├train_modified.csv
││││├XGBoostmodelstuning.ipynb
││││├
│││││├FeatureEngineering-checkpoint.ipynb
│││││└XGBoostmodelstuning-checkpoint.ipynb
│││├
││││├test.csv
││││├Titanic.ipynb
││││├train.csv
││││├
│││││└Titanic-checkpoint.ipynb
│││├
││││├Kaggle_Bicycle_Example.ipynb
││││├kaggle_bike_competition_train.csv
││││├
│││││└Kaggle_Bicycle_Example-checkpoint.ipynb
││││├
│││││├Kaggle_Bicycle_Example_34_0.png
│││││├Kaggle_Bicycle_Example_42_0.png
│││││├Kaggle_Bicycle_Example_43_0.png
│││││├Kaggle_Bicycle_Example_44_0.png
│││││├Kaggle_Bicycle_Example_45_0.png
│││││├Kaggle_Bicycle_Example_46_1.png
│││││├Kaggle_Bicycle_Example_47_1.png
│││││└Kaggle_Bicycle_Example_49_1.png
│├
││├
│││├data_description.txt
│││├
│││├
││││├sample_submission.csv
││││├test.csv
││││└train.csv
│││├
││││├house_price.html
││││├house_price.ipynb
││││├house_price_advanced.html
││││├house_price_advanced.ipynb
││││├
│││││├house_price_advanced-checkpoint.ipynb
│││││└house_price-checkpoint.ipynb
││├
│││├
│││├
││││├Combined_News_DJIA.csv
││││├DJIA_table.csv
││││└RedditNews.csv
│││├
││││├news_stock.html
││││├news_stock.ipynb
││││├
│││││└news_stock-checkpoint.ipynb
│├
││├A危azu-CTR-Prediction-LR.zip
││├feature.search
││├feature.search_ads
││├feature_map.search_ads
││├generate_train_feature_mapper.py
││├generate_train_feature_reducer.py
││├kaggle-A危azu-rank1.zip
││├kaggle-A危azu-rank2.zip
││├search_ads_feature.sample
││├search_click_data.sample
││├Spark-Criteo-CTR-Prediction.ipynb
││└xgb_ads.conf
│├
││├
││├
│││├news_stock.html
│││├news_stock_advanced.html
│││├searchrelevance.ipynb
│││├searchrelevance_advanced.ipynb
│││├search+relevance.html
│││├search+relevance_advanced.html
│││├
││││├searchrelevance_advanced-checkpoint.ipynb
││││└searchrelevance-checkpoint.ipynb
│├
││├energy_forecasting_notebooks.zip
││└subway_prediction_notebook.zip
│├
││├cat_dog.html
││├char_rnn.html
││├image_search.html
││├Kaggle第06课：走起~深度学习.pdf
││├Kaggle第06课：走起~深度学习.pptx
││├news_stock_advanced.html
││├word_rnn.html
││├
│││├chi_square.png
│││└RGBHistogram.jpg
││├
│││├cats-vs-dogs.txt
│││├sample_submission.csv
│││├test.zip
│││└train.zip
│├
││├data.zip
││├Kaggleeventrecommendationcompetition.ipynb
││├kaggle-event-recommendation-rank1.zip
││└Rossmann_Store_Sales_competition.ipynb
│├
││└PPD_RiskControl_Competition.zip
├
│├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
│├Kaggle第06课：走起~深度学习.pptx
│├
││├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第02课：经济金融相关问题.pdf
│├
││├kaggle-2014-criteo.pdf
││├kaggle-A危azu.pdf
││├predicting-clicks-facebook.pdf
││├阿里妈妈：大数据下的广告排序技术及实践.pdf
││├百度凤巢：DNN在凤巢CTR预估中的应用.pdf
││├从FM到FFM.pdf
││├第3课--排序与CTR预估.pdf
││├京东电商广告和推荐系统的机器学习系统实践.pdf
││└腾讯广点通：效果广告中的机器学习技术.pdf
│├
││└Kaggle第四课.pdf
│├
││└第5课：能源预测与分配问题.pdf
│├
││└第7课：推荐与销量预测相关问题.pdf
│├
││├第8课：金融风控问题.pdf
││└金融风控大赛解决方案.pdf

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