Evaluating Classification and Regression Systems, Part 2

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Evaluating Classification and Regression Systems, Part 2 [TutsNode.com] - Evaluating Classification and Regression Systems, Part 2
  • 2.3 Metrics from the Binary Confusion Matrix-1_46mxjpcf.mp4 (566.9 MB)
  • 4.3 Understanding the Default Regression Metric R^2-1_ur2v7gpn.mp4 (460.3 MB)
  • 1.2 Overfitting_Underfitting II -‚ÄØVarying Model Complexity-1_4jc84dfq.mp4 (454.5 MB)
  • 3.1 Multi-class Metric Averages-1_yuamn4h2.mp4 (402.3 MB)
  • 2.4 Understanding the ROC Curve and AUC-1_p7isvg1c.mp4 (401.7 MB)
  • 4.6 Case Study - A Regressor Comparison-1_ptued3au.mp4 (392.3 MB)
  • 1.8 Getting Graphical -‚ÄØLearning and Complexity Curves-1_064j1pbp.mp4 (363.8 MB)
  • 4.4 Errors and Residual Plots-1_6oyh6r9k.mp4 (348.7 MB)
  • 4.5 A Quick Pipeline and Standardization-1_owjtdyq9.mp4 (315.3 MB)
  • 3.5 Case Study - A Classifier Comparison-1_zp5u03mz.mp4 (297.1 MB)
  • 2.1 Baseline Classifiers and Classification Metrics-1_6zplg1w6.mp4 (262.2 MB)
  • 2.2 The Confusion Matrix-1_kosvwu6a.mp4 (257.0 MB)
  • 2.5 Comparing Classifiers with ROC and PR Curves-1_pf3lihbt.mp4 (249.0 MB)
  • 4.2 Regression Metrics - Custom Metrics and RMSE-1_y1esj7mf.mp4 (237.0 MB)
  • 1.7 Repeated Train-Test Splits-1_vcl9pphi.mp4 (219.7 MB)
  • 1.1 Overfitting_Underfitting I -‚ÄØSynthetic Data-1_yphjgmjr.mp4 (205.6 MB)
  • 3.2 Multi-class AUC - One-versus-Rest-1_ennowo9v.mp4 (198.9 MB)
  • 4.1 Baseline Regressors-1_d7296qus.mp4 (193.9 MB)
  • 1.6 Stratification-1_7661gb5m.mp4 (185.3 MB)
  • 3.3 Multi-class AUC - The Hand and Till Method-1_6a7v3mxv.mp4 (179.6 MB)
  • 3.4 Cumulative Response and Lift Curves-1_1laascbx.mp4 (178.6 MB)
  • 1.4 Cross-Validation-1_wcmktsak.mp4 (177.9 MB)
  • 1.3 Errors and Costs-1_364pwj7e.mp4 (149.0 MB)
  • 1.9 Graphical Cross-Validation-1_wb2bx7cm.mp4 (138.1 MB)
  • 1.5 Leave-One-Out Cross-Validation-1_hou0uzn8.mp4 (133.2 MB)
  • TutsNode.com.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
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Description


Overview

Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond following along in discussions to coding machine learning tasks. These videos show you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends.

You learn about the fundamental metrics used to evaluate general learning systems and specific metrics used in classification and regression. You will learn techniques for getting the most informative learning performance measures out of your data. You will come away with a strong toolbox of numerical and graphical techniques to understand how your learning system will perform on novel data.



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Evaluating Classification and Regression Systems, Part 2

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6.8 GB
seeders:10
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Evaluating Classification and Regression Systems, Part 2

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