Experimental Design for Data Analysis

• 03. Building and Training a Machine Learning Model/07. Building and Training a Regression Model in Python.mp4-23.26 MB
• 04. Understanding and Overcoming Common Problems in Data Modeling/06. Building Training and Evaluating a Classification Model.mp4-20.13 MB
• 03. Building and Training a Machine Learning Model/06. Building and Training a Regression Model for Price Prediction.mp4-19.86 MB
• 04. Understanding and Overcoming Common Problems in Data Modeling/05. Preparing and Processing Data.mp4-18.75 MB
• 06. Tuning Hyperparameters Using Cross Validation Scores/04. Hyperparameter Tuning a Decision Forest Classifier.mp4-16.95 MB
• 03. Building and Training a Machine Learning Model/05. Preprocessing and Preparing Data.mp4-16.08 MB
• 05. Leveraging Different Validation Strategies in Data Modeling/06. K-fold Cross-validation in scikit-learn.mp4-15.58 MB
• 05. Leveraging Different Validation Strategies in Data Modeling/04. Cross-validation Using Azure ML Studio.mp4-15.52 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.mp4-12.59 MB
• 03. Building and Training a Machine Learning Model/03. Loading and Visualizing Data.mp4-12.53 MB
• 03. Building and Training a Machine Learning Model/04. Exploring Relationships in Data.mp4-12 MB
• 02. Designing an Experiment for Data Analysis/04. Hypothesis Testing.mp4-11.93 MB
• 04. Understanding and Overcoming Common Problems in Data Modeling/02. Overfitting and Techniques to Mitigate Overfitting.mp4-10.58 MB
• 05. Leveraging Different Validation Strategies in Data Modeling/05. K-fold Cross-validation and Variants.mp4-9.91 MB
• 05. Leveraging Different Validation Strategies in Data Modeling/07. Repeated K-fold Cross-validation in scikit-learn.mp4-9.43 MB
• 05. Leveraging Different Validation Strategies in Data Modeling/09. Group K-fold in scikit-learn.mp4-8.77 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.mp4-7.64 MB
• 04. Understanding and Overcoming Common Problems in Data Modeling/04. The ROC Curve.mp4-6.17 MB
• 02. Designing an Experiment for Data Analysis/05. T-tests.mp4-5.19 MB
• experimental-design-data-analysis.zip-5.15 MB
• 05. Leveraging Different Validation Strategies in Data Modeling/03. Singular Cross-validation.mp4-5.15 MB
• 06. Tuning Hyperparameters Using Cross Validation Scores/03. Decision Trees.mp4-4.56 MB
• 02. Designing an Experiment for Data Analysis/03. Connecting the Dots with Data.mp4-4.35 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.mp4-3.48 MB
• 06. Tuning Hyperparameters Using Cross Validation Scores/01. Module Overview.mp4-3.43 MB
• 06. Tuning Hyperparameters Using Cross Validation Scores/02. Hyperparameter Tuning.mp4-3.19 MB
• 02. Designing an Experiment for Data Analysis/08. Summary.mp4-2.58 MB
• 03. Building and Training a Machine Learning Model/01. Module Overview.mp4-2.35 MB
• 04. Understanding and Overcoming Common Problems in Data Modeling/07. Summary.mp4-2.09 MB
• 02. Designing an Experiment for Data Analysis/01. Module Overview.mp4-2.04 MB
• 05. Leveraging Different Validation Strategies in Data Modeling/01. Module Overview.mp4-1.99 MB
• 05. Leveraging Different Validation Strategies in Data Modeling/10. Summary.mp4-1.98 MB
• 03. Building and Training a Machine Learning Model/08. Summary.mp4-1.96 MB
• 02. Designing an Experiment for Data Analysis/02. Prerequisites and Course Outline.mp4-1.75 MB
• 06. Tuning Hyperparameters Using Cross Validation Scores/06. Summary and Further Study.mp4-1.74 MB
• 04. Understanding and Overcoming Common Problems in Data Modeling/01. Module Overview.mp4-1.72 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-66.59 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-58.84 KB
• 02. Designing an Experiment for Data Analysis/04. Hypothesis Testing.srt-57.71 KB
• 05. Leveraging Different Validation Strategies in Data Modeling/06. K-fold Cross-validation in scikit-learn.srt-54.73 KB
• 04. Understanding and Overcoming Common Problems in Data Modeling/02. Overfitting and Techniques to Mitigate Overfitting.srt-52.76 KB
• 06. Tuning Hyperparameters Using Cross Validation Scores/04. Hyperparameter Tuning a Decision Forest Classifier.srt-51.06 KB
• 03. Building and Training a Machine Learning Model/05. Preprocessing and Preparing Data.srt-48.82 KB