Experimental Design for Data Analysis
File List
- 03. Building and Training a Machine Learning Model/07. Building and Training a Regression Model in Python.mp4 23.3 MB
- 04. Understanding and Overcoming Common Problems in Data Modeling/06. Building Training and Evaluating a Classification Model.mp4 20.1 MB
- 03. Building and Training a Machine Learning Model/06. Building and Training a Regression Model for Price Prediction.mp4 19.9 MB
- 04. Understanding and Overcoming Common Problems in Data Modeling/05. Preparing and Processing Data.mp4 18.8 MB
- 06. Tuning Hyperparameters Using Cross Validation Scores/04. Hyperparameter Tuning a Decision Forest Classifier.mp4 16.9 MB
- 03. Building and Training a Machine Learning Model/05. Preprocessing and Preparing Data.mp4 16.1 MB
- 05. Leveraging Different Validation Strategies in Data Modeling/06. K-fold Cross-validation in scikit-learn.mp4 15.6 MB
- 05. Leveraging Different Validation Strategies in Data Modeling/04. Cross-validation Using Azure ML Studio.mp4 15.5 MB
- 03. Building and Training a Machine Learning Model/02. Getting Started with Azure ML Studio.mp4 13.5 MB
- 06. Tuning Hyperparameters Using Cross Validation Scores/05. Tuning and Scoring Multiple Models.mp4 12.6 MB
- 05. Leveraging Different Validation Strategies in Data Modeling/08. Stratified K-fold Cross-validation in scikit-learn.mp4 12.6 MB
- 03. Building and Training a Machine Learning Model/03. Loading and Visualizing Data.mp4 12.5 MB
- 03. Building and Training a Machine Learning Model/04. Exploring Relationships in Data.mp4 12.0 MB
- 02. Designing an Experiment for Data Analysis/04. Hypothesis Testing.mp4 11.9 MB
- 04. Understanding and Overcoming Common Problems in Data Modeling/02. Overfitting and Techniques to Mitigate Overfitting.mp4 10.6 MB
- 05. Leveraging Different Validation Strategies in Data Modeling/05. K-fold Cross-validation and Variants.mp4 9.9 MB
- 05. Leveraging Different Validation Strategies in Data Modeling/07. Repeated K-fold Cross-validation in scikit-learn.mp4 9.4 MB
- 05. Leveraging Different Validation Strategies in Data Modeling/09. Group K-fold in scikit-learn.mp4 8.8 MB
- 02. Designing an Experiment for Data Analysis/07. Designing a Machine Learning Experiment.mp4 8.5 MB
- 04. Understanding and Overcoming Common Problems in Data Modeling/03. Accuracy, Precision, and Recall.mp4 7.8 MB
- 02. Designing an Experiment for Data Analysis/06. ANOVA.mp4 7.6 MB
- 04. Understanding and Overcoming Common Problems in Data Modeling/04. The ROC Curve.mp4 6.2 MB
- 02. Designing an Experiment for Data Analysis/05. T-tests.mp4 5.2 MB
- experimental-design-data-analysis.zip 5.2 MB
- 05. Leveraging Different Validation Strategies in Data Modeling/03. Singular Cross-validation.mp4 5.1 MB
- 06. Tuning Hyperparameters Using Cross Validation Scores/03. Decision Trees.mp4 4.6 MB
- 02. Designing an Experiment for Data Analysis/03. Connecting the Dots with Data.mp4 4.3 MB
- 01. Course Overview/01. Course Overview.mp4 3.7 MB
- 05. Leveraging Different Validation Strategies in Data Modeling/02. Cross-validation in the ML Workflow.mp4 3.5 MB
- 06. Tuning Hyperparameters Using Cross Validation Scores/01. Module Overview.mp4 3.4 MB
- 06. Tuning Hyperparameters Using Cross Validation Scores/02. Hyperparameter Tuning.mp4 3.2 MB
- 02. Designing an Experiment for Data Analysis/08. Summary.mp4 2.6 MB
- 03. Building and Training a Machine Learning Model/01. Module Overview.mp4 2.4 MB
- 04. Understanding and Overcoming Common Problems in Data Modeling/07. Summary.mp4 2.1 MB
- 02. Designing an Experiment for Data Analysis/01. Module Overview.mp4 2.0 MB
- 05. Leveraging Different Validation Strategies in Data Modeling/01. Module Overview.mp4 2.0 MB
- 05. Leveraging Different Validation Strategies in Data Modeling/10. Summary.mp4 2.0 MB
- 03. Building and Training a Machine Learning Model/08. Summary.mp4 2.0 MB
- 02. Designing an Experiment for Data Analysis/02. Prerequisites and Course Outline.mp4 1.7 MB
- 06. Tuning Hyperparameters Using Cross Validation Scores/06. Summary and Further Study.mp4 1.7 MB
- 04. Understanding and Overcoming Common Problems in Data Modeling/01. Module Overview.mp4 1.7 MB
- 03. Building and Training a Machine Learning Model/07. Building and Training a Regression Model in Python.srt 70.3 KB
- 03. Building and Training a Machine Learning Model/06. Building and Training a Regression Model for Price Prediction.srt 66.6 KB
- 04. Understanding and Overcoming Common Problems in Data Modeling/06. Building Training and Evaluating a Classification Model.srt 63.1 KB
- 04. Understanding and Overcoming Common Problems in Data Modeling/05. Preparing and Processing Data.srt 58.8 KB
- 02. Designing an Experiment for Data Analysis/04. Hypothesis Testing.srt 57.7 KB
- 05. Leveraging Different Validation Strategies in Data Modeling/06. K-fold Cross-validation in scikit-learn.srt 54.7 KB
- 04. Understanding and Overcoming Common Problems in Data Modeling/02. Overfitting and Techniques to Mitigate Overfitting.srt 52.8 KB
- 06. Tuning Hyperparameters Using Cross Validation Scores/04. Hyperparameter Tuning a Decision Forest Classifier.srt 51.1 KB
- 03. Building and Training a Machine Learning Model/05. Preprocessing and Preparing Data.srt 48.8 KB
- 05. Leveraging Different Validation Strategies in Data Modeling/05. K-fold Cross-validation and Variants.srt 45.2 KB
- 03. Building and Training a Machine Learning Model/03. Loading and Visualizing Data.srt 44.9 KB
- 03. Building and Training a Machine Learning Model/02. Getting Started with Azure ML Studio.srt 43.2 KB
- 05. Leveraging Different Validation Strategies in Data Modeling/04. Cross-validation Using Azure ML Studio.srt 42.8 KB
- 02. Designing an Experiment for Data Analysis/07. Designing a Machine Learning Experiment.srt 42.4 KB
- 06. Tuning Hyperparameters Using Cross Validation Scores/05. Tuning and Scoring Multiple Models.srt 40.0 KB
- 03. Building and Training a Machine Learning Model/04. Exploring Relationships in Data.srt 38.7 KB
- 05. Leveraging Different Validation Strategies in Data Modeling/08. Stratified K-fold Cross-validation in scikit-learn.srt 36.7 KB
- 04. Understanding and Overcoming Common Problems in Data Modeling/03. Accuracy, Precision, and Recall.srt 36.7 KB
- 02. Designing an Experiment for Data Analysis/06. ANOVA.srt 35.4 KB
- 05. Leveraging Different Validation Strategies in Data Modeling/07. Repeated K-fold Cross-validation in scikit-learn.srt 31.3 KB
- 05. Leveraging Different Validation Strategies in Data Modeling/09. Group K-fold in scikit-learn.srt 30.5 KB
- 04. Understanding and Overcoming Common Problems in Data Modeling/04. The ROC Curve.srt 30.4 KB
- 05. Leveraging Different Validation Strategies in Data Modeling/03. Singular Cross-validation.srt 26.2 KB
- 02. Designing an Experiment for Data Analysis/05. T-tests.srt 25.1 KB
- 06. Tuning Hyperparameters Using Cross Validation Scores/03. Decision Trees.srt 24.5 KB
- 02. Designing an Experiment for Data Analysis/03. Connecting the Dots with Data.srt 22.7 KB
- 06. Tuning Hyperparameters Using Cross Validation Scores/01. Module Overview.srt 16.4 KB
- 06. Tuning Hyperparameters Using Cross Validation Scores/02. Hyperparameter Tuning.srt 15.8 KB
- 05. Leveraging Different Validation Strategies in Data Modeling/02. Cross-validation in the ML Workflow.srt 13.7 KB
- 01. Course Overview/01. Course Overview.srt 13.2 KB
- 03. Building and Training a Machine Learning Model/01. Module Overview.srt 12.1 KB
- 02. Designing an Experiment for Data Analysis/08. Summary.srt 12.0 KB
- 04. Understanding and Overcoming Common Problems in Data Modeling/07. Summary.srt 11.8 KB
- 03. Building and Training a Machine Learning Model/08. Summary.srt 10.5 KB
- 02. Designing an Experiment for Data Analysis/02. Prerequisites and Course Outline.srt 10.2 KB
- 05. Leveraging Different Validation Strategies in Data Modeling/10. Summary.srt 9.6 KB
- 06. Tuning Hyperparameters Using Cross Validation Scores/06. Summary and Further Study.srt 9.0 KB
- 04. Understanding and Overcoming Common Problems in Data Modeling/01. Module Overview.srt 8.8 KB
- 05. Leveraging Different Validation Strategies in Data Modeling/01. Module Overview.srt 8.7 KB
- 02. Designing an Experiment for Data Analysis/01. Module Overview.srt 8.6 KB
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