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Experimental Design for Data Analysis

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文件列表

  1. 03. Building and Training a Machine Learning Model/07. Building and Training a Regression Model in Python.mp4 23MB
  2. 04. Understanding and Overcoming Common Problems in Data Modeling/06. Building Training and Evaluating a Classification Model.mp4 20MB
  3. 03. Building and Training a Machine Learning Model/06. Building and Training a Regression Model for Price Prediction.mp4 20MB
  4. 04. Understanding and Overcoming Common Problems in Data Modeling/05. Preparing and Processing Data.mp4 19MB
  5. 06. Tuning Hyperparameters Using Cross Validation Scores/04. Hyperparameter Tuning a Decision Forest Classifier.mp4 17MB
  6. 03. Building and Training a Machine Learning Model/05. Preprocessing and Preparing Data.mp4 16MB
  7. 05. Leveraging Different Validation Strategies in Data Modeling/06. K-fold Cross-validation in scikit-learn.mp4 16MB
  8. 05. Leveraging Different Validation Strategies in Data Modeling/04. Cross-validation Using Azure ML Studio.mp4 16MB
  9. 03. Building and Training a Machine Learning Model/02. Getting Started with Azure ML Studio.mp4 13MB
  10. 06. Tuning Hyperparameters Using Cross Validation Scores/05. Tuning and Scoring Multiple Models.mp4 13MB
  11. 05. Leveraging Different Validation Strategies in Data Modeling/08. Stratified K-fold Cross-validation in scikit-learn.mp4 13MB
  12. 03. Building and Training a Machine Learning Model/03. Loading and Visualizing Data.mp4 13MB
  13. 03. Building and Training a Machine Learning Model/04. Exploring Relationships in Data.mp4 12MB
  14. 02. Designing an Experiment for Data Analysis/04. Hypothesis Testing.mp4 12MB
  15. 04. Understanding and Overcoming Common Problems in Data Modeling/02. Overfitting and Techniques to Mitigate Overfitting.mp4 11MB
  16. 05. Leveraging Different Validation Strategies in Data Modeling/05. K-fold Cross-validation and Variants.mp4 10MB
  17. 05. Leveraging Different Validation Strategies in Data Modeling/07. Repeated K-fold Cross-validation in scikit-learn.mp4 9MB
  18. 05. Leveraging Different Validation Strategies in Data Modeling/09. Group K-fold in scikit-learn.mp4 9MB
  19. 02. Designing an Experiment for Data Analysis/07. Designing a Machine Learning Experiment.mp4 9MB
  20. 04. Understanding and Overcoming Common Problems in Data Modeling/03. Accuracy, Precision, and Recall.mp4 8MB
  21. 02. Designing an Experiment for Data Analysis/06. ANOVA.mp4 8MB
  22. 04. Understanding and Overcoming Common Problems in Data Modeling/04. The ROC Curve.mp4 6MB
  23. 02. Designing an Experiment for Data Analysis/05. T-tests.mp4 5MB
  24. experimental-design-data-analysis.zip 5MB
  25. 05. Leveraging Different Validation Strategies in Data Modeling/03. Singular Cross-validation.mp4 5MB
  26. 06. Tuning Hyperparameters Using Cross Validation Scores/03. Decision Trees.mp4 5MB
  27. 02. Designing an Experiment for Data Analysis/03. Connecting the Dots with Data.mp4 4MB
  28. 01. Course Overview/01. Course Overview.mp4 4MB
  29. 05. Leveraging Different Validation Strategies in Data Modeling/02. Cross-validation in the ML Workflow.mp4 3MB
  30. 06. Tuning Hyperparameters Using Cross Validation Scores/01. Module Overview.mp4 3MB
  31. 06. Tuning Hyperparameters Using Cross Validation Scores/02. Hyperparameter Tuning.mp4 3MB
  32. 02. Designing an Experiment for Data Analysis/08. Summary.mp4 3MB
  33. 03. Building and Training a Machine Learning Model/01. Module Overview.mp4 2MB
  34. 04. Understanding and Overcoming Common Problems in Data Modeling/07. Summary.mp4 2MB
  35. 02. Designing an Experiment for Data Analysis/01. Module Overview.mp4 2MB
  36. 05. Leveraging Different Validation Strategies in Data Modeling/01. Module Overview.mp4 2MB
  37. 05. Leveraging Different Validation Strategies in Data Modeling/10. Summary.mp4 2MB
  38. 03. Building and Training a Machine Learning Model/08. Summary.mp4 2MB
  39. 02. Designing an Experiment for Data Analysis/02. Prerequisites and Course Outline.mp4 2MB
  40. 06. Tuning Hyperparameters Using Cross Validation Scores/06. Summary and Further Study.mp4 2MB
  41. 04. Understanding and Overcoming Common Problems in Data Modeling/01. Module Overview.mp4 2MB
  42. 03. Building and Training a Machine Learning Model/07. Building and Training a Regression Model in Python.srt 70KB
  43. 03. Building and Training a Machine Learning Model/06. Building and Training a Regression Model for Price Prediction.srt 67KB
  44. 04. Understanding and Overcoming Common Problems in Data Modeling/06. Building Training and Evaluating a Classification Model.srt 63KB
  45. 04. Understanding and Overcoming Common Problems in Data Modeling/05. Preparing and Processing Data.srt 59KB
  46. 02. Designing an Experiment for Data Analysis/04. Hypothesis Testing.srt 58KB
  47. 05. Leveraging Different Validation Strategies in Data Modeling/06. K-fold Cross-validation in scikit-learn.srt 55KB
  48. 04. Understanding and Overcoming Common Problems in Data Modeling/02. Overfitting and Techniques to Mitigate Overfitting.srt 53KB
  49. 06. Tuning Hyperparameters Using Cross Validation Scores/04. Hyperparameter Tuning a Decision Forest Classifier.srt 51KB
  50. 03. Building and Training a Machine Learning Model/05. Preprocessing and Preparing Data.srt 49KB
  51. 05. Leveraging Different Validation Strategies in Data Modeling/05. K-fold Cross-validation and Variants.srt 45KB
  52. 03. Building and Training a Machine Learning Model/03. Loading and Visualizing Data.srt 45KB
  53. 03. Building and Training a Machine Learning Model/02. Getting Started with Azure ML Studio.srt 43KB
  54. 05. Leveraging Different Validation Strategies in Data Modeling/04. Cross-validation Using Azure ML Studio.srt 43KB
  55. 02. Designing an Experiment for Data Analysis/07. Designing a Machine Learning Experiment.srt 42KB
  56. 06. Tuning Hyperparameters Using Cross Validation Scores/05. Tuning and Scoring Multiple Models.srt 40KB
  57. 03. Building and Training a Machine Learning Model/04. Exploring Relationships in Data.srt 39KB
  58. 05. Leveraging Different Validation Strategies in Data Modeling/08. Stratified K-fold Cross-validation in scikit-learn.srt 37KB
  59. 04. Understanding and Overcoming Common Problems in Data Modeling/03. Accuracy, Precision, and Recall.srt 37KB
  60. 02. Designing an Experiment for Data Analysis/06. ANOVA.srt 35KB
  61. 05. Leveraging Different Validation Strategies in Data Modeling/07. Repeated K-fold Cross-validation in scikit-learn.srt 31KB
  62. 05. Leveraging Different Validation Strategies in Data Modeling/09. Group K-fold in scikit-learn.srt 31KB
  63. 04. Understanding and Overcoming Common Problems in Data Modeling/04. The ROC Curve.srt 30KB
  64. 05. Leveraging Different Validation Strategies in Data Modeling/03. Singular Cross-validation.srt 26KB
  65. 02. Designing an Experiment for Data Analysis/05. T-tests.srt 25KB
  66. 06. Tuning Hyperparameters Using Cross Validation Scores/03. Decision Trees.srt 25KB
  67. 02. Designing an Experiment for Data Analysis/03. Connecting the Dots with Data.srt 23KB
  68. 06. Tuning Hyperparameters Using Cross Validation Scores/01. Module Overview.srt 16KB
  69. 06. Tuning Hyperparameters Using Cross Validation Scores/02. Hyperparameter Tuning.srt 16KB
  70. 05. Leveraging Different Validation Strategies in Data Modeling/02. Cross-validation in the ML Workflow.srt 14KB
  71. 01. Course Overview/01. Course Overview.srt 13KB
  72. 03. Building and Training a Machine Learning Model/01. Module Overview.srt 12KB
  73. 02. Designing an Experiment for Data Analysis/08. Summary.srt 12KB
  74. 04. Understanding and Overcoming Common Problems in Data Modeling/07. Summary.srt 12KB
  75. 03. Building and Training a Machine Learning Model/08. Summary.srt 10KB
  76. 02. Designing an Experiment for Data Analysis/02. Prerequisites and Course Outline.srt 10KB
  77. 05. Leveraging Different Validation Strategies in Data Modeling/10. Summary.srt 10KB
  78. 06. Tuning Hyperparameters Using Cross Validation Scores/06. Summary and Further Study.srt 9KB
  79. 04. Understanding and Overcoming Common Problems in Data Modeling/01. Module Overview.srt 9KB
  80. 05. Leveraging Different Validation Strategies in Data Modeling/01. Module Overview.srt 9KB
  81. 02. Designing an Experiment for Data Analysis/01. Module Overview.srt 9KB