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种子名称： O'Reilly - Advanced Machine Learning with scikit learn
文件类型： 视频
文件数目： 46个文件
文件大小： 708.36 MB
收录时间： 2017-5-31 16:06
已经下载： 3次
资源热度： 148
最近下载： 2024-12-2 19:40

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O'Reilly - Advanced Machine Learning with scikit learn.torrent

01. Introduction/01_01-What To Expect And About The Author.mp412.51MB

01. Introduction/01_02-Setup.mp45.86MB

01. Introduction/01_03-The Classifier Interface.mp427.18MB

01. Introduction/01_04-The Regressor Interface.mp410.81MB

01. Introduction/01_05-The Transformer Interface.mp47.9MB

01. Introduction/01_06-The Cluster Interface.mp422.34MB

01. Introduction/01_07-The Manifold Interface.mp410.96MB

01. Introduction/01_08-scikitLearn Interface Summary.mp411.31MB

01. Introduction/01_09-CrossValidation With Cross_Val_Score.mp420.32MB

01. Introduction/01_10-Parameter Searches With GridSearchCV.mp419.34MB

01. Introduction/01_11-How To Access Your Working Files.mp426.38MB

02. Model Complexity, Overfitting And Underfitting/02_01-What Is Model Complexity And Overfitting.mp47.25MB

02. Model Complexity, Overfitting And Underfitting/02_02-Linear Models InDepth.mp433.58MB

02. Model Complexity, Overfitting And Underfitting/02_03-Kernel SVMs InDepth.mp421.89MB

02. Model Complexity, Overfitting And Underfitting/02_04-Random Forests InDepth.mp415.13MB

02. Model Complexity, Overfitting And Underfitting/02_05-Learning Curves For Analyzing Model Complexity.mp412.64MB

02. Model Complexity, Overfitting And Underfitting/02_06-Validation Curves For Analyzing Model Parameters.mp47.61MB

02. Model Complexity, Overfitting And Underfitting/02_07-Efficient Parameter Search With EstimatorCV Objects.mp418.08MB

03. Pipelines/03_01-Motivation Of Using Pipelines.mp49.69MB

03. Pipelines/03_02-Defining A Pipeline And Basic Usage.mp419.08MB

03. Pipelines/03_03-CrossValidation With Pipelines.mp47.76MB

03. Pipelines/03_04-Parameter Selection With Pipelines.mp416.74MB

04. Advanced Metrics And Imbalanced Classes/04_01-Be Mindful Of Default Metrics.mp420.6MB

04. Advanced Metrics And Imbalanced Classes/04_02-More Evaluation Methods For Classification.mp414.57MB

04. Advanced Metrics And Imbalanced Classes/04_03-AUC.mp419.69MB

04. Advanced Metrics And Imbalanced Classes/04_04-Defining Custom Metrics.mp420.44MB

05. Model Selection For Unsupervised Learning/05_01-Guidelines For Unsupervised Model Selection.mp421.63MB

05. Model Selection For Unsupervised Learning/05_02-Model Selection For Density Models.mp418.17MB

05. Model Selection For Unsupervised Learning/05_03-Model Selection For Clustering.mp414.02MB

06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_01-Why Real Data Is Messy.mp419.04MB

06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_02-OneHot Encoding For Categorical Data.mp418.12MB

06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_03-Working With Dictionaries.mp46.42MB

06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_04-Handling Incomplete Data.mp414.68MB

07. Handling Text Data/07_01-Motivation.mp48.03MB

07. Handling Text Data/07_02-BagOfWords Representations.mp419.1MB

07. Handling Text Data/07_03-Text Classification For Sentiment Analysis Part 1.mp425.36MB

07. Handling Text Data/07_04-Text Classification For Sentiment Analysis Part 2.mp412.97MB

07. Handling Text Data/07_05-The Hashing Trick.mp49.11MB

07. Handling Text Data/07_06-Other Representations Distributed Word Representations.mp45.08MB

08. Out Of Core Learning/08_01-The TradeOffs Of Out Of Core Learning.mp410.8MB

08. Out Of Core Learning/08_02-The scikitLearn Interface For Out Of Core Learning.mp414.67MB

08. Out Of Core Learning/08_03-Kernel Approximations For LargeScale NonLinear Classification.mp416.04MB

08. Out Of Core Learning/08_04-Subsample And Transform Supervised Transformations For Out Of Core Learning.mp418.35MB

08. Out Of Core Learning/08_05-Application OutOfCore Text Classification.mp418.4MB

09. Conclusion/09_01-Summary.mp410.07MB

09. Conclusion/09_02-Where To Go From Here.mp48.62MB