Files Included :
01 practical-data-science-with-matlab (21.9 MB)
02 instructor-introduction (10.99 MB)
05 introduction-to-supervised-machine-learning (28.82 MB)
02 introduction-to-the-taxi-data (29.37 MB)
04 creating-and-cleaning-features (17.87 MB)
01 introduction-to-regression (15.71 MB)
03 using-the-regression-learner-app (20.49 MB)
01 customizing-model-parameters (21.55 MB)
02 evaluating-regression-models (14.53 MB)
04 evaluate-your-model-in-matlab (20.39 MB)
05 summary-of-regression (4.05 MB)
02 introduction-to-classification (17.35 MB)
04 using-the-classification-learner-app (25.13 MB)
01 evaluating-classification-models (24.5 MB)
03 evaluating-classification-models-in-matlab (13.17 MB)
01 training-a-multiclass-model (14.68 MB)
04 summary-of-classification (5.65 MB)
02 addressing-underfitting-and-overfitting (15.13 MB)
03 using-validation-data-during-training (11.5 MB)
01 embedded-methods-for-feature-selection (16.78 MB)
02 using-regularization-to-prevent-overfitting (13.2 MB)
01 introduction-to-ensemble-models (8.52 MB)
02 training-ensemble-models (20.03 MB)
01 introduction-to-hyperparameters (11.79 MB)
02 optimizing-hyperparameters (19.67 MB)
03 evaluating-and-using-your-model (27.62 MB)
04 summary-of-module-3 (7.12 MB)
03 handling-class-imbalance (18.99 MB)
06 reducing-specific-errors-using-cost-matrices (15.91 MB)
01 integrating-your-model (13.84 MB)
04 a-discussion-with-heather (63.76 MB)
05 summary-of-predictive-modeling-and-machine-learning (19 MB)
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