Coursera - Applied Machine Learning in Python

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844.1 MB | 00:31:15 | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
02 introduction  (31.4 MB)
03 whats-new  (3.07 MB)
05 key-concepts-in-machine-learning  (46.26 MB)
06 python-tools-for-machine-learning  (11.15 MB)
08 an-example-machine-learning-problem  (27.42 MB)
09 examining-the-data  (35.26 MB)
10 k-nearest-neighbors-classification  (32.61 MB)
01 introduction-to-supervised-machine-learning  (34.63 MB)
02 overfitting-and-underfitting  (15.48 MB)
03 supervised-learning-datasets  (10.6 MB)
04 k-nearest-neighbors-classification-and-regression  (18.09 MB)
05 linear-regression-least-squares  (25 MB)
06 linear-regression-ridge-lasso-and-polynomial-regression  (44.94 MB)
07 logistic-regression  (15.88 MB)
08 linear-classifiers-support-vector-machines  (17.79 MB)
09 multi-class-classification  (14.74 MB)
10 kernelized-support-vector-machines  (34.77 MB)
11 cross-validation  (19.89 MB)
12 decision-trees  (32.44 MB)
15 one-hot-encoding-optional  (16.74 MB)
01 model-evaluation-selection  (41.4 MB)
02 confusion-matrices-basic-evaluation-metrics  (19.63 MB)
03 classifier-decision-functions  (10.1 MB)
04 precision-recall-and-roc-curves  (8.54 MB)
05 multi-class-evaluation  (10.97 MB)
06 regression-evaluation  (16.42 MB)
08 model-selection-optimizing-classifiers-for-different-evaluation-metrics  (16.8 MB)
09 model-calibration-optional  (46.4 MB)
01 naive-bayes-classifiers  (21.16 MB)
02 random-forests  (24.48 MB)
03 gradient-boosted-decision-trees  (10.3 MB)
04 neural-networks  (12.55 MB)
07 deep-learning-optional  (12.4 MB)
10 data-leakage  (12.08 MB)
01 introduction  (10.12 MB)
02 dimensionality-reduction-and-manifold-learning  (12.73 MB)
03 clustering  (23.63 MB)
01 conclusion  (11.18 MB)

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