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
第二节【】.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
5.链表递归栈【】.mp4
4.树【】.mp4
7.图论（上）【】.mp4
julyedu【】.com 解压密码
6.查找排序【】.mp4
8.图论下【】.mp4
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
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