24、python-机器学习-进阶实战

📁 唐宇迪-机器学习-进阶实战-资料
  📁 1.数据特征
    📁 数值特征
      📁 .ipynb_checkpoints
        Feature Engineering on Numeric Data-checkpoint.ipynb  [238.7KB]
        Feature Engineering on Temporal Data-checkpoint.ipynb  [23.1KB]
        Feature Engineering on Text Data-checkpoint.ipynb  [47.6KB]
        Feature Selection-checkpoint.ipynb  [29.2KB]
        数值特征-checkpoint.ipynb  [216.9KB]
        图像特征-checkpoint.ipynb  [791.8KB]
        特征预处理-checkpoint.ipynb  [11.7KB]
        文本特征-checkpoint.ipynb  [42.5KB]
      📁 datasets
        cat.png  [119.5KB]
        desktop.ini  [138B]
        dog.png  [54.6KB]
        fcc_2016_coder_survey_subset.csv  [2.6M]
        item_popularity.csv  [147B]
        Pokemon.csv  [46.9KB]
        song_views.csv  [27.8KB]
        vgsales.csv  [1.3M]
      数值特征.ipynb  [217.2KB]
      图像特征.ipynb  [259.5KB]
      特征预处理.ipynb  [11.7KB]
      文本特征.ipynb  [44.5KB]
  📁 2.GBDT提升算法
    GBDT.pdf  [763.8KB]
  📁 3.xgboost-gbdt-lightgbm
    GBDT.zip  [51.6M]
  📁 4.使用lightgbm进行饭店流量预测
    GBDT【整理不易‖记得关注：CunWorKNotes】.zip  [51.6M]
  📁 5.人口普查数据集项目实战-收入预测
    1.png  [186.7KB]
    2.png  [62.6KB]
    3.png  [698.3KB]
    机器学习数据分析模板.ipynb  [4.2M]
  📁 6.降维算法-线性判别分析
    9-LDA与PCA算法.pdf  [1M]
    降维算法.zip  [443.7KB]
  📁 7.贝叶斯优化及其工具包使用
    贝叶斯优化：Hyperopt.zip  [17.4M]
  📁 8.贝叶斯优化实战
    贝叶斯优化：Hyperopt.zip  [17.4M]
  📁 9.EM算法
    10-EM算法.pdf  [811.4KB]
  📁 10.HMM隐马尔科夫模型
    HMM.pdf  [1M]
  📁 11.HMM案例实战
    📁 HMM
      📁 __pycache__
        data.cpython-36.pyc  [216B]
        get_hmm_param.cpython-36.pyc  [2.4KB]
      data.py  [318B]
      get_hmm_param.py  [2.6KB]
      hmm_start.py  [823B]
    data2.csv  [130.7KB]
    hmm实践.ipynb  [5.8KB]
    时间序列.ipynb  [189KB]
  📁 12.推荐系统
    推荐系统.pdf  [2.1M]
  📁 13.音乐推荐系统实战
    📁 Python实现音乐推荐系统
      📁 .ipynb_checkpoints
        推荐系统-checkpoint.ipynb  [344.8KB]
      📁 __pycache__
        Recommenders.cpython-36.pyc  [5KB]
      1.png  [45.3KB]
      2.png  [30.4KB]
      3.png  [43KB]
      4.png  [12KB]
      5.png  [3.6KB]
      6.png  [60.3KB]
      7.png  [77.3KB]
      8.png  [68.8KB]
      老版.ipynb  [344.8KB]
      recommendation_engines.py  [13.7KB]
      Recommenders.py  [9.2KB]
      song_playcount_df.csv  [8.5M]
      推荐系统.ipynb  [363.5KB]
      track_metadata.db  [711.6M]
      track_metadata_df_sub.csv  [5.9M]
      train_triplets【花费时间‖免费获取：cunLove.cn】.txt  [2.8G]
      triplet_dataset_sub_song.csv  [648.3M]
      user_playcount_df.csv  [44.1M]
  📁 14.基于统计分析的电影推荐
    电影推荐【良心整理‖免费获取：cunloVe.cn】.zip  [10M]
  📁 15.学习曲线
    📁 学习曲线
      📁 .ipynb_checkpoints
        学习曲线（Bias和Variance）-checkpoint.ipynb  [137.1KB]
      1.png  [52.1KB]
      2.png  [36KB]
      3.png  [56.1KB]
      4.png  [24.9KB]
      5.png  [45.1KB]
      6.png  [93.1KB]
      7.png  [75.3KB]
      8.png  [58.4KB]
      9.png  [5.2KB]
      10.png  [39.9KB]
      11.png  [67.5KB]
      Folds5x2_pp【耗时整理‖ cunlove.cn】.xlsx  [1.9M]
      学习曲线（Bias和Variance）.ipynb  [137.1KB]
  📁 16.NLP-文本特征方法对比
    clean_data.csv  [1.1M]
    GoogleNews-vectors-negative300.bin  [3.4G]
    socialmedia_relevant_cols.csv  [1.2M]
    socialmedia_relevant_cols_clean.csv  [1.2M]
    socialmedia_relevant_cols_clean2.csv  [1.2M]
    自然语言处理方法对比.ipynb  [1M]
  📁 17.使用word2vec分类任务
    word2vec.zip  [84.6M]
  📁 18.Tensorflow自己打造word2vec
    📁 word2vec
      word2vec.zip  [32.6M]
  📁 19.制作自己常用工具包
    📁 特征筛选
      📁 .ipynb_checkpoints
        Feature Selector Usage-checkpoint.ipynb  [433.5KB]
        工具-checkpoint.ipynb  [353.3KB]
      📁 __pycache__
        feature_selector.cpython-36.pyc  [19.5KB]
      📁 data
        AirQualityUCI.csv  [615.1KB]
        caravan-insurance-challenge.csv  [1.7M]
        credit_example.csv  [5.2M]
      📁 feature_selector
        __init__.py  [46B]
        feature_selector.py  [27.9KB]
      📁 images
      工具.ipynb  [396KB]
  📁 20.数据处理与特征工程
    📁 机器学习项目实战流程
      📁 .ipynb_checkpoints
        Exploratory_Work-checkpoint.ipynb  [1.4M]
        机器学习项目实战-1-数据预处理-checkpoint.ipynb  [1.2M]
        机器学习项目实战-2-建模-checkpoint.ipynb  [362KB]
        机器学习项目实战-3-分析-checkpoint.ipynb  [3.6M]
      📁 auto_ml
        tpot_exported_pipeline.py  [1.3KB]
      📁 data
        cleaned_data.csv  [6.4M]
        Energy_and_Water_Data_Disclosure_for_Local_Law_84_2017__Data_for_Calendar_Year_2016_.csv  [7.9M]
        no_score.csv  [373.3KB]
        testing_features.csv  [571.8KB]
        testing_labels.csv  [16.6KB]
        training_features.csv  [1.3M]
        training_labels.csv  [38.8KB]
        X_test.csv  [802.9KB]
        X_train.csv  [1.8M]
        Y_test.csv  [13.9KB]
        Y_train.csv  [32.3KB]
      📁 images
        annotated_individual_node.PNG  [84.6KB]
        correlation_examples.png  [15.4KB]
        cover_auto_ml.jpg  [221.3KB]
        cover_one.jpg  [259.6KB]
        cover_three.jpg  [703.4KB]
        cover_two.jpg  [255KB]
        data_formatted_with_score.PNG  [22.5KB]
        density_boroughs.png  [108KB]
        density_type.png  [94.8KB]
        df_info.PNG  [41.2KB]
        feature_importances.PNG  [16.9KB]
        feature_importances_graph.png  [84KB]
        feature_pairs.png  [145.4KB]
        formatted_train_data.PNG  [41.4KB]
        individual_node.png  [7.2KB]
        intrepretability_vs_accuracy.png  [50.3KB]
        kfold_cv.png  [80.5KB]
        lime_wrong_explanation.PNG  [30.7KB]
        local_explanation_one.png  [92.5KB]
        missing_values.PNG  [23.5KB]
      2016_nyc_benchmarking_data_disclosure_definitions.pdf  [99KB]
      Building Data Report.pdf  [769.6KB]
      hw_assignment【耗时整理‖免费分享：Cunlove.cn】.docx  [13.9KB]
      hw_assignment【优质合集‖免费获取：cunlove.cN】.pdf  [12.4KB]
      机器学习项目实战-1-数据预处理.ipynb  [1.2M]
      机器学习项目实战-2-建模.ipynb  [318.2KB]
      机器学习项目实战-3-分析.ipynb  [3.6M]
1 数据特征.mp4  [236.1M]
2 GBDT提升算法【优质资源‖关注：cunWorkNotes 解锁】.mp4  [62.1M]
3 xgboost-gbdt-lightgbm提.mp4  [72.6M]
4 使用lightgbm进行饭店流【资源精选‖更多关注：CunworkNotes】.mp4  [105M]
5 人口普查数据集项目实战.mp4  [221.1M]
6 降维算法-线性判别分析.mp4  [82.5M]
7 贝叶斯优化及其工具包使用.mp4  [125.9M]
8 贝叶斯优化实战.mp4  [93.8M]
10 HMM隐马尔科夫模型【良心整理‖免费获取：cunloVe.cn】.mp4  [117.2M]
11 HMM案例实战.mp4  [66.6M]
12 推荐系统【花费时间‖免费获取：cunLove.cn】.mp4  [71.6M]
13 音乐推荐系统实战【资源精选‖更多关注：CunworkNotes】.mp4  [208.4M]
14 基于统计分析的电影推荐.mp4  [233.2M]
15 学习曲线.mp4  [74.2M]
16 NLP-文本特征方法对比.mp4  [140.9M]
17 使用word2vec分类任务【优质合集‖免费获取：cunlove.cN】.mp4  [156.2M]
18 和 19 章.mp4  [180.6M]
20 机器学习项目实战-数据处.mp4  [195.6M]
21 机器学习项目实战-建模与.mp4  [154.6M]