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)