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Evaluating Classification and Regression Systems, Part 2

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Other > Training

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English

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6.81 GB

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848f26f3747e15a0cfc9b13ea23acf576c48b9a0

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15-05-2021 05:50

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[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/1.1 Overfitting_Underfitting I -‚ÄØSynthetic Data-1_yphjgmjr.mp4	205.58 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/1.2 Overfitting_Underfitting II -‚ÄØVarying Model Complexity-1_4jc84dfq.mp4	454.52 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/1.3 Errors and Costs-1_364pwj7e.mp4	149.01 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/1.4 Cross-Validation-1_wcmktsak.mp4	177.91 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/1.5 Leave-One-Out Cross-Validation-1_hou0uzn8.mp4	133.22 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/1.6 Stratification-1_7661gb5m.mp4	185.29 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/1.7 Repeated Train-Test Splits-1_vcl9pphi.mp4	219.65 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/1.8 Getting Graphical -‚ÄØLearning and Complexity Curves-1_064j1pbp.mp4	363.78 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/1.9 Graphical Cross-Validation-1_wb2bx7cm.mp4	138.09 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/2.1 Baseline Classifiers and Classification Metrics-1_6zplg1w6.mp4	262.19 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/2.2 The Confusion Matrix-1_kosvwu6a.mp4	256.97 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/2.3 Metrics from the Binary Confusion Matrix-1_46mxjpcf.mp4	566.86 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/2.4 Understanding the ROC Curve and AUC-1_p7isvg1c.mp4	401.70 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/2.5 Comparing Classifiers with ROC and PR Curves-1_pf3lihbt.mp4	248.96 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/3.1 Multi-class Metric Averages-1_yuamn4h2.mp4	402.29 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/3.2 Multi-class AUC - One-versus-Rest-1_ennowo9v.mp4	198.94 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/3.3 Multi-class AUC - The Hand and Till Method-1_6a7v3mxv.mp4	179.61 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/3.4 Cumulative Response and Lift Curves-1_1laascbx.mp4	178.60 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/3.5 Case Study - A Classifier Comparison-1_zp5u03mz.mp4	297.14 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/4.1 Baseline Regressors-1_d7296qus.mp4	193.95 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/4.2 Regression Metrics - Custom Metrics and RMSE-1_y1esj7mf.mp4	236.97 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/4.3 Understanding the Default Regression Metric R^2-1_ur2v7gpn.mp4	460.34 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/4.4 Errors and Residual Plots-1_6oyh6r9k.mp4	348.70 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/4.5 A Quick Pipeline and Standardization-1_owjtdyq9.mp4	315.25 MB
[TutsNode.com] - Evaluating Classification and Regression Systems, Part 2/4.6 Case Study - A Regressor Comparison-1_ptued3au.mp4	392.26 MB

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.