O'Reilly - Advanced Machine Learning with scikit learn
- 收录时间:2018-03-16 15:17:01
- 文件大小:766MB
- 下载次数:229
- 最近下载:2021-01-14 04:40:32
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文件列表
- Working Files/Advanced_Machine_Learning_with_scikit_learn_Working_Files.zip 57MB
- 02. Model Complexity, Overfitting And Underfitting/02_02-Linear Models InDepth.mp4 34MB
- 01. Introduction/01_03-The Classifier Interface.mp4 27MB
- 01. Introduction/01_11-How To Access Your Working Files.mp4 26MB
- 07. Handling Text Data/07_03-Text Classification For Sentiment Analysis Part 1.mp4 25MB
- 01. Introduction/01_06-The Cluster Interface.mp4 22MB
- 02. Model Complexity, Overfitting And Underfitting/02_03-Kernel SVMs InDepth.mp4 22MB
- 05. Model Selection For Unsupervised Learning/05_01-Guidelines For Unsupervised Model Selection.mp4 22MB
- 04. Advanced Metrics And Imbalanced Classes/04_01-Be Mindful Of Default Metrics.mp4 21MB
- 04. Advanced Metrics And Imbalanced Classes/04_04-Defining Custom Metrics.mp4 20MB
- 01. Introduction/01_09-CrossValidation With Cross_Val_Score.mp4 20MB
- 04. Advanced Metrics And Imbalanced Classes/04_03-AUC.mp4 20MB
- 01. Introduction/01_10-Parameter Searches With GridSearchCV.mp4 19MB
- 07. Handling Text Data/07_02-BagOfWords Representations.mp4 19MB
- 03. Pipelines/03_02-Defining A Pipeline And Basic Usage.mp4 19MB
- 06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_01-Why Real Data Is Messy.mp4 19MB
- 08. Out Of Core Learning/08_05-Application OutOfCore Text Classification.mp4 18MB
- 08. Out Of Core Learning/08_04-Subsample And Transform Supervised Transformations For Out Of Core Learning.mp4 18MB
- 05. Model Selection For Unsupervised Learning/05_02-Model Selection For Density Models.mp4 18MB
- 06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_02-OneHot Encoding For Categorical Data.mp4 18MB
- 02. Model Complexity, Overfitting And Underfitting/02_07-Efficient Parameter Search With EstimatorCV Objects.mp4 18MB
- 03. Pipelines/03_04-Parameter Selection With Pipelines.mp4 17MB
- 08. Out Of Core Learning/08_03-Kernel Approximations For LargeScale NonLinear Classification.mp4 16MB
- 02. Model Complexity, Overfitting And Underfitting/02_04-Random Forests InDepth.mp4 15MB
- 06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_04-Handling Incomplete Data.mp4 15MB
- 08. Out Of Core Learning/08_02-The scikitLearn Interface For Out Of Core Learning.mp4 15MB
- 04. Advanced Metrics And Imbalanced Classes/04_02-More Evaluation Methods For Classification.mp4 15MB
- 05. Model Selection For Unsupervised Learning/05_03-Model Selection For Clustering.mp4 14MB
- 07. Handling Text Data/07_04-Text Classification For Sentiment Analysis Part 2.mp4 13MB
- 02. Model Complexity, Overfitting And Underfitting/02_05-Learning Curves For Analyzing Model Complexity.mp4 13MB
- 01. Introduction/01_01-What To Expect And About The Author.mp4 13MB
- 01. Introduction/01_08-scikitLearn Interface Summary.mp4 11MB
- 01. Introduction/01_07-The Manifold Interface.mp4 11MB
- 01. Introduction/01_04-The Regressor Interface.mp4 11MB
- 08. Out Of Core Learning/08_01-The TradeOffs Of Out Of Core Learning.mp4 11MB
- 09. Conclusion/09_01-Summary.mp4 10MB
- 03. Pipelines/03_01-Motivation Of Using Pipelines.mp4 10MB
- 07. Handling Text Data/07_05-The Hashing Trick.mp4 9MB
- 09. Conclusion/09_02-Where To Go From Here.mp4 9MB
- 07. Handling Text Data/07_01-Motivation.mp4 8MB
- 01. Introduction/01_05-The Transformer Interface.mp4 8MB
- 03. Pipelines/03_03-CrossValidation With Pipelines.mp4 8MB
- 02. Model Complexity, Overfitting And Underfitting/02_06-Validation Curves For Analyzing Model Parameters.mp4 8MB
- 02. Model Complexity, Overfitting And Underfitting/02_01-What Is Model Complexity And Overfitting.mp4 7MB
- 06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_03-Working With Dictionaries.mp4 6MB
- 01. Introduction/01_02-Setup.mp4 6MB
- 07. Handling Text Data/07_06-Other Representations Distributed Word Representations.mp4 5MB
