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种子名称： Experimental Design for Data Analysis
文件类型： 视频
文件数目： 40个文件
文件大小： 343.32 MB
收录时间： 2025-5-18 01:14
已经下载： 3次
资源热度： 23
最近下载： 2025-5-28 15:59

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Experimental Design for Data Analysis.torrent

01. Course Overview/01. Course Overview.mp43.7MB

02. Designing an Experiment for Data Analysis/01. Module Overview.mp42.04MB

02. Designing an Experiment for Data Analysis/02. Prerequisites and Course Outline.mp41.75MB

02. Designing an Experiment for Data Analysis/03. Connecting the Dots with Data.mp44.35MB

02. Designing an Experiment for Data Analysis/04. Hypothesis Testing.mp411.93MB

02. Designing an Experiment for Data Analysis/05. T-tests.mp45.19MB

02. Designing an Experiment for Data Analysis/06. ANOVA.mp47.64MB

02. Designing an Experiment for Data Analysis/07. Designing a Machine Learning Experiment.mp48.5MB

02. Designing an Experiment for Data Analysis/08. Summary.mp42.58MB

03. Building and Training a Machine Learning Model/01. Module Overview.mp42.35MB

03. Building and Training a Machine Learning Model/02. Getting Started with Azure ML Studio.mp413.5MB

03. Building and Training a Machine Learning Model/03. Loading and Visualizing Data.mp412.53MB

03. Building and Training a Machine Learning Model/04. Exploring Relationships in Data.mp412MB

03. Building and Training a Machine Learning Model/05. Preprocessing and Preparing Data.mp416.08MB

03. Building and Training a Machine Learning Model/06. Building and Training a Regression Model for Price Prediction.mp419.86MB

03. Building and Training a Machine Learning Model/07. Building and Training a Regression Model in Python.mp423.26MB

03. Building and Training a Machine Learning Model/08. Summary.mp41.96MB

04. Understanding and Overcoming Common Problems in Data Modeling/01. Module Overview.mp41.72MB

04. Understanding and Overcoming Common Problems in Data Modeling/02. Overfitting and Techniques to Mitigate Overfitting.mp410.58MB

04. Understanding and Overcoming Common Problems in Data Modeling/03. Accuracy, Precision, and Recall.mp47.8MB

04. Understanding and Overcoming Common Problems in Data Modeling/04. The ROC Curve.mp46.17MB

04. Understanding and Overcoming Common Problems in Data Modeling/05. Preparing and Processing Data.mp418.75MB

04. Understanding and Overcoming Common Problems in Data Modeling/06. Building Training and Evaluating a Classification Model.mp420.13MB

04. Understanding and Overcoming Common Problems in Data Modeling/07. Summary.mp42.09MB

05. Leveraging Different Validation Strategies in Data Modeling/01. Module Overview.mp41.99MB

05. Leveraging Different Validation Strategies in Data Modeling/02. Cross-validation in the ML Workflow.mp43.48MB

05. Leveraging Different Validation Strategies in Data Modeling/03. Singular Cross-validation.mp45.15MB

05. Leveraging Different Validation Strategies in Data Modeling/04. Cross-validation Using Azure ML Studio.mp415.52MB

05. Leveraging Different Validation Strategies in Data Modeling/05. K-fold Cross-validation and Variants.mp49.91MB

05. Leveraging Different Validation Strategies in Data Modeling/06. K-fold Cross-validation in scikit-learn.mp415.58MB

05. Leveraging Different Validation Strategies in Data Modeling/07. Repeated K-fold Cross-validation in scikit-learn.mp49.43MB

05. Leveraging Different Validation Strategies in Data Modeling/08. Stratified K-fold Cross-validation in scikit-learn.mp412.59MB

05. Leveraging Different Validation Strategies in Data Modeling/09. Group K-fold in scikit-learn.mp48.77MB

05. Leveraging Different Validation Strategies in Data Modeling/10. Summary.mp41.98MB

06. Tuning Hyperparameters Using Cross Validation Scores/01. Module Overview.mp43.43MB

06. Tuning Hyperparameters Using Cross Validation Scores/02. Hyperparameter Tuning.mp43.19MB

06. Tuning Hyperparameters Using Cross Validation Scores/03. Decision Trees.mp44.56MB

06. Tuning Hyperparameters Using Cross Validation Scores/04. Hyperparameter Tuning a Decision Forest Classifier.mp416.95MB

06. Tuning Hyperparameters Using Cross Validation Scores/05. Tuning and Scoring Multiple Models.mp412.6MB

06. Tuning Hyperparameters Using Cross Validation Scores/06. Summary and Further Study.mp41.74MB