617.45 MB | 00:08:31 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
02 introduction-to-the-course.mp4 (9.13 MB)
03 what-does-a-classifier-actually-do.mp4 (22.12 MB)
04 classification-in-scikit-learn.mp4 (12.09 MB)
01 what-are-decision-trees.mp4 (21.99 MB)
02 generalization-and-overfitting.mp4 (29.46 MB)
01 classification-using-k-nearest-neighbours.mp4 (33.1 MB)
02 distance-measures.mp4 (36.74 MB)
04 weekly-summary.mp4 (11.53 MB)
01 line-fitting.mp4 (19.15 MB)
02 optimal-line-fitting.mp4 (22.89 MB)
03 loss-and-convexity.mp4 (19.64 MB)
04 gradient-descent.mp4 (24.82 MB)
01 nonlinear-features-and-model-complexity.mp4 (23.24 MB)
02 bias-and-variance-tradeoff.mp4 (24.19 MB)
03 regularizers.mp4 (22.87 MB)
01 loss-for-classification.mp4 (23.6 MB)
02 weekly-summary.mp4 (14.28 MB)
01 logistic-regression.mp4 (11.31 MB)
02 neural-networks.mp4 (32.74 MB)
01 hinge-loss.mp4 (16.93 MB)
02 basics-of-support-vector-machines.mp4 (26.58 MB)
01 kernels.mp4 (24.74 MB)
02 weekly-summary.mp4 (9.53 MB)
01 regression-assessment.mp4 (11.56 MB)
02 classification-assessment.mp4 (12.42 MB)
03 learning-curves.mp4 (20.42 MB)
01 testing-your-models.mp4 (24.99 MB)
02 cross-validation.mp4 (14.62 MB)
01 parameter-tuning-and-grid-search.mp4 (16.42 MB)
02 model-parameters.mp4 (11.95 MB)
03 weekly-summary.mp4 (9.22 MB)]
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