[ DevCourseWeb.com ] Udemy - Data pre-processing for Machine Learning in Python

磁力链接/BT种子名称

[ DevCourseWeb.com ] Udemy - Data pre-processing for Machine Learning in Python

磁力链接/BT种子简介

种子哈希：c3caa5f1bef84cdef0dcd44e0dc80900fe02a8e6
文件大小： 1.97G
已经下载：542次
下载速度：极快
收录时间：2022-04-15
最近下载：2025-07-19

移花宫入口

移花宫.com邀月.com怜星.com花无缺.comyhgbt.icuyhgbt.top

磁力链接下载

magnet:?xt=urn:btih:C3CAA5F1BEF84CDEF0DCD44E0DC80900FE02A8E6
推荐使用PIKPAK网盘下载资源，10TB超大空间，不限制资源，无限次数离线下载，视频在线观看

下载BT种子文件

磁力链接 迅雷下载 PIKPAK在线播放 世界之窗 91视频 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 抖阴破解版 极乐禁地 91短视频 TikTok成人版 PornHub 草榴社区 哆哔涩漫 呦乐园 萝莉岛

最近搜索

文件列表

  • ~Get Your Files Here !/9. A complete pipeline/1. An example of a complete pipeline.mp4 127.1 MB
  • ~Get Your Files Here !/3. Encoding of the categorical features/2. One-hot encoding.mp4 120.3 MB
  • ~Get Your Files Here !/2. Data cleaning/6. ColumnTransformer and make_column_selector.mp4 92.7 MB
  • ~Get Your Files Here !/8. Filter-based feature selection/6. Feature importance according to a model.mp4 91.6 MB
  • ~Get Your Files Here !/2. Data cleaning/7. Exercises.mp4 84.7 MB
  • ~Get Your Files Here !/5. Pipelines/3. Exercises.mp4 82.6 MB
  • ~Get Your Files Here !/5. Pipelines/2. Pipelines and ColumnTransformer together.mp4 82.5 MB
  • ~Get Your Files Here !/8. Filter-based feature selection/2. Numerical features, numerical target.mp4 81.7 MB
  • ~Get Your Files Here !/4. Transformations of the numerical features/6. Exercise.mp4 80.5 MB
  • ~Get Your Files Here !/3. Encoding of the categorical features/5. Exercise.mp4 78.0 MB
  • ~Get Your Files Here !/6. Scaling/2. Normalization, Standardization, Robust scaling.mp4 74.6 MB
  • ~Get Your Files Here !/8. Filter-based feature selection/4. Categorical features, numerical target.mp4 74.6 MB
  • ~Get Your Files Here !/7. Principal Component Analysis/2. How to perform PCA.mp4 64.8 MB
  • ~Get Your Files Here !/2. Data cleaning/5. KNN blank filling.mp4 63.9 MB
  • ~Get Your Files Here !/4. Transformations of the numerical features/3. Binning.mp4 63.4 MB
  • ~Get Your Files Here !/2. Data cleaning/3. Cleaning the numerical features.mp4 62.0 MB
  • ~Get Your Files Here !/10. Oversampling/2. How to perform SMOTE.mp4 59.7 MB
  • ~Get Your Files Here !/8. Filter-based feature selection/5. Categorical features, categorical target.mp4 59.7 MB
  • ~Get Your Files Here !/8. Filter-based feature selection/9. Exercises.mp4 56.4 MB
  • ~Get Your Files Here !/8. Filter-based feature selection/3. Numerical features, categorical target.mp4 54.7 MB
  • ~Get Your Files Here !/6. Scaling/3. Exercise.mp4 53.1 MB
  • ~Get Your Files Here !/4. Transformations of the numerical features/2. Power Transformation.mp4 51.1 MB
  • ~Get Your Files Here !/4. Transformations of the numerical features/5. Applying an arbitrary transformation.mp4 44.1 MB
  • ~Get Your Files Here !/3. Encoding of the categorical features/3. Ordinal encoding.mp4 42.0 MB
  • ~Get Your Files Here !/5. Pipelines/1. Define a transformation pipeline.mp4 40.7 MB
  • ~Get Your Files Here !/10. Oversampling/3. Exercise.mp4 37.2 MB
  • ~Get Your Files Here !/1. Introduction/5. Jupyter notebooks.mp4 36.3 MB
  • ~Get Your Files Here !/7. Principal Component Analysis/3. Exercise.mp4 34.3 MB
  • ~Get Your Files Here !/8. Filter-based feature selection/1. Introduction to feature selection.mp4 30.0 MB
  • ~Get Your Files Here !/2. Data cleaning/2. Selecting numerical and categorical variables.mp4 28.9 MB
  • ~Get Your Files Here !/10. Oversampling/1. Introduction to SMOTE.mp4 20.6 MB
  • ~Get Your Files Here !/6. Scaling/1. Introduction to scaling.mp4 19.9 MB
  • ~Get Your Files Here !/7. Principal Component Analysis/1. Introduction to PCA.mp4 19.8 MB
  • ~Get Your Files Here !/1. Introduction/1. Introduction to the course.mp4 18.4 MB
  • ~Get Your Files Here !/2. Data cleaning/4. Cleaning the categorical features.mp4 17.8 MB
  • ~Get Your Files Here !/4. Transformations of the numerical features/4. Binarizing.mp4 12.1 MB
  • ~Get Your Files Here !/1. Introduction/2. Numerical and categorical variables.mp4 12.1 MB
  • ~Get Your Files Here !/4. Transformations of the numerical features/1. Introduction to transformations.mp4 11.4 MB
  • ~Get Your Files Here !/3. Encoding of the categorical features/4. Label encoding of the target variable.mp4 10.6 MB
  • ~Get Your Files Here !/2. Data cleaning/1. Introduction to data cleaning.mp4 10.0 MB
  • ~Get Your Files Here !/3. Encoding of the categorical features/1. Introduction to the encoding of categorical variables.mp4 5.7 MB
  • ~Get Your Files Here !/1. Introduction/3.2 sample_dataset.csv 99.4 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/4.1 Categorical features numerical target.ipynb 45.5 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/2.1 Power Transform.ipynb 44.5 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/5.1 Categorical features categorical target.ipynb 44.2 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/2.1 Numerical target numerical feature.ipynb 42.1 kB
  • ~Get Your Files Here !/2. Data cleaning/4.1 Cleaning the categorical features.ipynb 35.1 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/3.1 Binning.ipynb 31.0 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/6.1 Feature importance according to model.ipynb 26.9 kB
  • ~Get Your Files Here !/7. Principal Component Analysis/2.1 PCA.ipynb 25.9 kB
  • ~Get Your Files Here !/2. Data cleaning/7.1 Exercises.ipynb 24.1 kB
  • ~Get Your Files Here !/3. Encoding of the categorical features/2. One-hot encoding.srt 20.2 kB
  • ~Get Your Files Here !/9. A complete pipeline/1. An example of a complete pipeline.srt 18.3 kB
  • ~Get Your Files Here !/6. Scaling/2.1 Scaling techniques.ipynb 14.6 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/4.1 Binarizer.ipynb 13.6 kB
  • ~Get Your Files Here !/2. Data cleaning/6. ColumnTransformer and make_column_selector.srt 13.5 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/3.1 Numerical features categorical target.ipynb 13.3 kB
  • ~Get Your Files Here !/3. Encoding of the categorical features/5. Exercise.srt 12.4 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/5.1 FunctionTransformer.ipynb 12.2 kB
  • ~Get Your Files Here !/6. Scaling/2. Normalization, Standardization, Robust scaling.srt 11.8 kB
  • ~Get Your Files Here !/7. Principal Component Analysis/3.1 Exercises.ipynb 11.4 kB
  • ~Get Your Files Here !/5. Pipelines/2. Pipelines and ColumnTransformer together.srt 11.4 kB
  • ~Get Your Files Here !/9. A complete pipeline/1.1 A complete pipeline.ipynb 11.3 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/3. Binning.srt 11.2 kB
  • ~Get Your Files Here !/3. Encoding of the categorical features/2.1 One-hot encoding.ipynb 11.0 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/6. Feature importance according to a model.srt 11.0 kB
  • ~Get Your Files Here !/5. Pipelines/3. Exercises.srt 10.9 kB
  • ~Get Your Files Here !/2. Data cleaning/5. KNN blank filling.srt 10.8 kB
  • ~Get Your Files Here !/2. Data cleaning/3. Cleaning the numerical features.srt 10.8 kB
  • ~Get Your Files Here !/10. Oversampling/2. How to perform SMOTE.srt 10.4 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/6. Exercise.srt 10.3 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/2. Numerical features, numerical target.srt 9.6 kB
  • ~Get Your Files Here !/2. Data cleaning/7. Exercises.srt 9.6 kB
  • ~Get Your Files Here !/1. Introduction/5. Jupyter notebooks.srt 9.6 kB
  • ~Get Your Files Here !/5. Pipelines/1. Define a transformation pipeline.srt 9.5 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/4. Categorical features, numerical target.srt 9.4 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/6.1 Exercises.ipynb 9.0 kB
  • ~Get Your Files Here !/10. Oversampling/2.1 How to do SMOTE.ipynb 8.9 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/2. Power Transformation.srt 8.9 kB
  • ~Get Your Files Here !/7. Principal Component Analysis/2. How to perform PCA.srt 8.8 kB
  • ~Get Your Files Here !/1. Introduction/3.1 sample_dataset_bins.csv 8.7 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/9. Exercises.srt 8.6 kB
  • ~Get Your Files Here !/3. Encoding of the categorical features/3. Ordinal encoding.srt 8.0 kB
  • ~Get Your Files Here !/2. Data cleaning/3.1 Cleaning the numerical features.ipynb 7.8 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/5. Applying an arbitrary transformation.srt 7.3 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/1. Introduction to feature selection.srt 7.2 kB
  • ~Get Your Files Here !/2. Data cleaning/6.1 ColumnTransformer.ipynb 7.0 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/5. Categorical features, categorical target.srt 7.0 kB
  • ~Get Your Files Here !/2. Data cleaning/5.1 Cleaning with KNN.ipynb 6.7 kB
  • ~Get Your Files Here !/6. Scaling/3. Exercise.srt 6.7 kB
  • ~Get Your Files Here !/5. Pipelines/3.1 Exercises.ipynb 6.3 kB
  • ~Get Your Files Here !/7. Principal Component Analysis/3. Exercise.srt 6.0 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/3. Numerical features, categorical target.srt 6.0 kB
  • ~Get Your Files Here !/10. Oversampling/3. Exercise.srt 5.7 kB
  • ~Get Your Files Here !/5. Pipelines/2.1 Pipelines and ColumnTransformer together .ipynb 5.7 kB
  • ~Get Your Files Here !/10. Oversampling/1. Introduction to SMOTE.srt 5.3 kB
  • ~Get Your Files Here !/3. Encoding of the categorical features/5.1 Exercises.ipynb 5.0 kB
  • ~Get Your Files Here !/10. Oversampling/3.1 Exercises.ipynb 5.0 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/9.1 Exercises.ipynb 5.0 kB
  • ~Get Your Files Here !/2. Data cleaning/2.1 Select numerical and categorical variables.ipynb 4.6 kB
  • ~Get Your Files Here !/6. Scaling/3.1 Exercise.ipynb 4.6 kB
  • ~Get Your Files Here !/5. Pipelines/1.1 Define a transformation pipeline.ipynb 4.3 kB
  • ~Get Your Files Here !/2. Data cleaning/2. Selecting numerical and categorical variables.srt 4.1 kB
  • ~Get Your Files Here !/7. Principal Component Analysis/1. Introduction to PCA.srt 4.0 kB
  • ~Get Your Files Here !/2. Data cleaning/4. Cleaning the categorical features.srt 3.8 kB
  • ~Get Your Files Here !/3. Encoding of the categorical features/3.1 OrdinalEncoder.ipynb 3.7 kB
  • ~Get Your Files Here !/1. Introduction/1. Introduction to the course.srt 3.5 kB
  • ~Get Your Files Here !/6. Scaling/1. Introduction to scaling.srt 3.2 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/1. Introduction to transformations.srt 2.7 kB
  • ~Get Your Files Here !/3. Encoding of the categorical features/4. Label encoding of the target variable.srt 2.5 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/4. Binarizing.srt 2.5 kB
  • ~Get Your Files Here !/2. Data cleaning/1. Introduction to data cleaning.srt 2.4 kB
  • ~Get Your Files Here !/1. Introduction/2. Numerical and categorical variables.srt 2.3 kB
  • ~Get Your Files Here !/3. Encoding of the categorical features/4.1 LabelEncoder.ipynb 1.6 kB
  • ~Get Your Files Here !/11. General guidelines/1. Practical suggestions.html 1.4 kB
  • ~Get Your Files Here !/3. Encoding of the categorical features/1. Introduction to the encoding of categorical variables.srt 1.3 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/7. A comment on mutual information.html 1.1 kB
  • ~Get Your Files Here !/4. Transformations of the numerical features/7. About power transformations.html 1.1 kB
  • ~Get Your Files Here !/8. Filter-based feature selection/8. A comment on feature selection with categorical variables.html 1.0 kB
  • ~Get Your Files Here !/1. Introduction/4. Required Python packages.html 919 Bytes
  • ~Get Your Files Here !/Bonus Resources.txt 386 Bytes
  • ~Get Your Files Here !/1. Introduction/3. The dataset.html 361 Bytes
  • Get Bonus Downloads Here.url 182 Bytes

随机展示

相关说明

本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。 网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！