Machine Learning Specialization

Files Included :

01 welcome.mp4 (10.64 MB)
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02 neurons-and-the-brain.mp4 (26.86 MB)
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03 demand-prediction.mp4 (24.19 MB)
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04 example-recognizing-images.mp4 (14.59 MB)
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01 neural-network-layer.mp4 (20.43 MB)
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02 more-complex-neural-networks.mp4 (17.03 MB)
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03 inference-making-predictions-forward-propagation.mp4 (12.55 MB)
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01 inference-in-code.mp4 (16.82 MB)
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02 data-in-tensorflow.mp4 (24.83 MB)
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03 building-a-neural-network.mp4 (24.4 MB)
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01 forward-prop-in-a-single-layer.mp4 (12.38 MB)
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02 general-implementation-of-forward-propagation.mp4 (21.33 MB)
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01 is-there-a-path-to-agi.mp4 (28.09 MB)
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01 how-neural-networks-are-implemented-efficiently.mp4 (12.24 MB)
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02 matrix-multiplication.mp4 (15.89 MB)
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03 matrix-multiplication-rules.mp4 (16.14 MB)
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04 matrix-multiplication-code.mp4 (13.38 MB)
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01 tensorflow-implementation.mp4 (11.4 MB)
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02 training-details.mp4 (24.08 MB)
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01 alternatives-to-the-sigmoid-activation.mp4 (11.96 MB)
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02 choosing-activation-functions.mp4 (23.39 MB)
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03 why-do-we-need-activation-functions.mp4 (12.93 MB)
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01 multiclass.mp4 (8.37 MB)
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02 softmax.mp4 (20.69 MB)
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03 neural-network-with-softmax-output.mp4 (15.03 MB)
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04 improved-implementation-of-softmax.mp4 (15.05 MB)
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05 classification-with-multiple-outputs-optional.mp4 (11.31 MB)
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01 advanced-optimization.mp4 (15.57 MB)
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02 additional-layer-types.mp4 (19.53 MB)
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01 what-is-a-derivative-optional.mp4 (38.3 MB)
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02 computation-graph-optional.mp4 (29.97 MB)
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03 larger-neural-network-example-optional.mp4 (26.11 MB)
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01 deciding-what-to-try-next.mp4 (11.45 MB)
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02 evaluating-a-model.mp4 (19.45 MB)
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01 diagnosing-bias-and-variance.mp4 (20.3 MB)
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02 regularization-and-bias-variance.mp4 (21.11 MB)
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03 establishing-a-baseline-level-of-performance.mp4 (19.39 MB)
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04 learning-curves.mp4 (23.28 MB)
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05 deciding-what-to-try-next-revisited.mp4 (28.02 MB)
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06 bias-variance-and-neural-networks.mp4 (26.94 MB)
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01 iterative-loop-of-ml-development.mp4 (14.81 MB)
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02 error-analysis.mp4 (17.51 MB)
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03 adding-data.mp4 (32.94 MB)
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04 transfer-learning-using-data-from-a-different-task.mp4 (19.02 MB)
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05 full-cycle-of-a-machine-learning-project.mp4 (16.35 MB)
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06 fairness-bias-and-ethics.mp4 (25.35 MB)
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01 error-metrics-for-skewed-datasets.mp4 (18.95 MB)
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02 trading-off-precision-and-recall.mp4 (22.17 MB)
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01 decision-tree-model.mp4 (14.76 MB)
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02 learning-process.mp4 (29.03 MB)
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01 measuring-purity.mp4 (15.97 MB)
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02 choosing-a-split-information-gain.mp4 (23.77 MB)
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03 putting-it-together.mp4 (18.41 MB)
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04 using-one-hot-encoding-of-categorical-features.mp4 (14.17 MB)
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05 continuous-valued-features.mp4 (15.89 MB)
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06 regression-trees-optional.mp4 (18.9 MB)
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01 using-multiple-decision-trees.mp4 (12.51 MB)
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02 sampling-with-replacement.mp4 (14.33 MB)
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03 random-forest-algorithm.mp4 (12.72 MB)
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04 xgboost.mp4 (21.08 MB)
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05 when-to-use-decision-trees.mp4 (17.47 MB)
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01 andrew-ng-and-chris-manning-on-natural-language-processing.mp4 (236.15 MB)
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01 welcome-to-machine-learning.mp4 (22.19 MB)
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02 applications-of-machine-learning.mp4 (33.45 MB)
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01 what-is-machine-learning.mp4 (25.98 MB)
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02 supervised-learning-part-1.mp4 (13.87 MB)
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03 supervised-learning-part-2.mp4 (14.39 MB)
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04 unsupervised-learning-part-1.mp4 (18.72 MB)
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05 unsupervised-learning-part-2.mp4 (8.25 MB)
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06 jupyter-notebooks.mp4 (19.9 MB)
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01 linear-regression-model-part-1.mp4 (20.27 MB)
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02 linear-regression-model-part-2.mp4 (16.22 MB)
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03 cost-function-formula.mp4 (16.73 MB)
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04 cost-function-intuition.mp4 (29.56 MB)
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05 visualizing-the-cost-function.mp4 (17.32 MB)
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06 visualization-examples.mp4 (17.18 MB)
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01 gradient-descent.mp4 (22.48 MB)
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02 implementing-gradient-descent.mp4 (20.91 MB)
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03 gradient-descent-intuition.mp4 (13.2 MB)
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04 learning-rate.mp4 (16.94 MB)
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05 gradient-descent-for-linear-regression.mp4 (16.38 MB)
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06 running-gradient-descent.mp4 (18.37 MB)
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01 multiple-features.mp4 (18.89 MB)
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02 vectorization-part-1.mp4 (17.27 MB)
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03 vectorization-part-2.mp4 (17.26 MB)
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04 gradient-descent-for-multiple-linear-regression.mp4 (19.36 MB)
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01 feature-scaling-part-1.mp4 (13.64 MB)
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02 feature-scaling-part-2.mp4 (14.39 MB)
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03 checking-gradient-descent-for-convergence.mp4 (10.99 MB)
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04 choosing-the-learning-rate.mp4 (16.31 MB)
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05 feature-engineering.mp4 (7.85 MB)
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06 polynomial-regression.mp4 (22.84 MB)
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01 motivations.mp4 (20.96 MB)
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02 logistic-regression.mp4 (21.48 MB)
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03 decision-boundary.mp4 (18.94 MB)
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01 cost-function-for-logistic-regression.mp4 (24.61 MB)
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02 simplified-cost-function-for-logistic-regression.mp4 (11.74 MB)
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01 gradient-descent-implementation.mp4 (12.76 MB)
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01 the-problem-of-overfitting.mp4 (23.97 MB)
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02 addressing-overfitting.mp4 (15.73 MB)
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03 cost-function-with-regularization.mp4 (17.1 MB)
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04 regularized-linear-regression.mp4 (19.81 MB)
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05 regularized-logistic-regression.mp4 (20.9 MB)
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01 andrew-ng-and-fei-fei-li-on-human-centered-ai.mp4 (214.72 MB)
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03 welcome-to-the-classification-course-a-part-of-the-machine-learning.mp4 (4.62 MB)
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04 what-is-this-course-about.mp4 (22.06 MB)
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05 impact-of-classification.mp4 (4.04 MB)
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01 course-overview.mp4 (11.43 MB)
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02 outline-of-first-half-of-course.mp4 (19.39 MB)
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03 outline-of-second-half-of-course.mp4 (20.16 MB)
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04 assumed-background.mp4 (12.02 MB)
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05 lets-get-started.mp4 (2.76 MB)
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02 linear-classifiers-a-motivating-example.mp4 (9.9 MB)
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03 intuition-behind-linear-classifiers.mp4 (12.31 MB)
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04 decision-boundaries.mp4 (11.1 MB)
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05 linear-classifier-model.mp4 (19.07 MB)
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06 effect-of-coefficient-values-on-decision-boundary.mp4 (7.65 MB)
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07 using-features-of-the-inputs.mp4 (8.72 MB)
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01 predicting-class-probabilities.mp4 (5.7 MB)
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02 review-of-basics-of-probabilities.mp4 (15.54 MB)
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03 review-of-basics-of-conditional-probabilities.mp4 (19.91 MB)
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04 using-probabilities-in-classification.mp4 (9.9 MB)
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01 predicting-class-probabilities-with-generalized-linear-models.mp4 (19.87 MB)
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02 the-sigmoid-or-logistic-link-function.mp4 (12.01 MB)
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03 logistic-regression-model.mp4 (11.4 MB)
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04 effect-of-coefficient-values-on-predicted-probabilities.mp4 (16.99 MB)
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05 overview-of-learning-logistic-regression-models.mp4 (7.87 MB)
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01 encoding-categorical-inputs.mp4 (14.56 MB)
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02 multiclass-classification-with-1-versus-all.mp4 (22.67 MB)
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01 recap-of-logistic-regression-classifier.mp4 (5.2 MB)
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01 predicting-sentiment-from-product-reviews amazon baby csv.zip (28.67 MB)
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01 predicting-sentiment-from-product-reviews amazon baby gl.zip (40.34 MB)
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01 predicting-sentiment-from-product-reviews amazon baby sframe.zip (40.33 MB)
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01 predicting-sentiment-from-product-reviews CLA02-NB01 ipynb.zip (6.46 KB)
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02 goal-learning-parameters-of-logistic-regression.mp4 (8.84 MB)
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03 intuition-behind-maximum-likelihood-estimation.mp4 (11.32 MB)
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04 data-likelihood.mp4 (18.3 MB)
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05 finding-best-linear-classifier-with-gradient-ascent.mp4 (9.94 MB)
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01 review-of-gradient-ascent.mp4 (15.46 MB)
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01 choosing-step-size.mp4 (14.4 MB)
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02 careful-with-step-sizes-that-are-too-large.mp4 (11.16 MB)
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03 rule-of-thumb-for-choosing-step-size.mp4 (9.36 MB)
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02 very-optional-expressing-the-log-likelihood.mp4 (7.06 MB)
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03 very-optional-deriving-probability-y-1-given-x.mp4 (4.96 MB)
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01 recap-of-learning-logistic-regression-classifiers.mp4 (6.96 MB)
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01 implementing-logistic-regression-from-scratch amazon baby subset csv.zip (9.16 MB)
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01 implementing-logistic-regression-from-scratch amazon baby subset gl.zip (12.73 MB)
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01 implementing-logistic-regression-from-scratch amazon baby subset sframe.zip (12.73 MB)
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01 implementing-logistic-regression-from-scratch CLA03-NB01 ipynb.zip (6.92 KB)
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01 implementing-logistic-regression-from-scratch important words json.zip (862 B)
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01 implementing-logistic-regression-from-scratch module-3-assignment-numpy-arrays npz.zip (1.16 MB)
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02 evaluating-a-classifier.mp4 (9.26 MB)
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03 review-of-overfitting-in-regression.mp4 (9.6 MB)
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04 overfitting-in-classification.mp4 (14.24 MB)
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02 visualizing-overconfident-predictions.mp4 (10.19 MB)
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02 l2-regularized-logistic-regression.mp4 (12.49 MB)
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01 sparse-logistic-regression-with-l1-regularization.mp4 (20.75 MB)
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01 logistic-regression-with-l2-regularization CLA04-NB01 ipynb.zip (7.46 KB)
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01 logistic-regression-with-l2-regularization important words json.zip (862 B)
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02 predicting-loan-defaults-with-decision-trees.mp4 (13.74 MB)
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03 intuition-behind-decision-trees.mp4 (5.9 MB)
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04 task-of-learning-decision-trees-from-data.mp4 (10.97 MB)
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01 recursive-greedy-algorithm.mp4 (11.15 MB)
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02 learning-a-decision-stump.mp4 (12.9 MB)
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03 selecting-best-feature-to-split-on.mp4 (15.14 MB)
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04 when-to-stop-recursing.mp4 (15.5 MB)
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01 making-predictions-with-decision-trees.mp4 (4.84 MB)
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02 multiclass-classification-with-decision-trees.mp4 (7 MB)
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01 threshold-splits-for-continuous-inputs.mp4 (17.28 MB)
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02 optional-picking-the-best-threshold-to-split-on.mp4 (9.47 MB)
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03 visualizing-decision-boundaries.mp4 (13.2 MB)
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01 recap-of-decision-trees.mp4 (3.43 MB)
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01 identifying-safe-loans-with-decision-trees CLA05-NB01 ipynb.zip (6.21 KB)
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01 identifying-safe-loans-with-decision-trees lending-club-data csv.zip (18.53 MB)
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01 identifying-safe-loans-with-decision-trees lending-club-data gl.zip (19.38 MB)
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01 identifying-safe-loans-with-decision-trees lending-club-data sframe.zip (19.38 MB)
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01 identifying-safe-loans-with-decision-trees module-5-assignment-1-train-idx json.zip (55.12 KB)
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01 identifying-safe-loans-with-decision-trees module-5-assignment-1-validation-idx json.zip (18.94 KB)
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01 implementing-binary-decision-trees CLA05-NB02 ipynb.zip (8.24 KB)
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01 implementing-binary-decision-trees lending-club-data csv.zip (18.53 MB)
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01 implementing-binary-decision-trees lending-club-data gl.zip (19.38 MB)
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01 implementing-binary-decision-trees lending-club-data sframe.zip (19.38 MB)
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01 implementing-binary-decision-trees module-5-assignment-2-test-idx json.zip (18.92 KB)
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01 implementing-binary-decision-trees module-5-assignment-2-train-idx json.zip (55.12 KB)
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01 implementing-binary-decision-trees module-5-decision-tree-assignment-2-blank ipynb.zip (7.74 KB)
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02 a-review-of-overfitting.mp4 (7.18 MB)
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03 overfitting-in-decision-trees.mp4 (16.82 MB)
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01 principle-of-occams-razor-learning-simpler-decision-trees.mp4 (14.5 MB)
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02 early-stopping-in-learning-decision-trees.mp4 (22.21 MB)
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01 optional-motivating-pruning.mp4 (21.63 MB)
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02 optional-pruning-decision-trees-to-avoid-overfitting.mp4 (16.72 MB)
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03 optional-tree-pruning-algorithm.mp4 (11.89 MB)
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01 decision-trees-in-practice CLA06-NB01 ipynb.zip (8.64 KB)
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01 decision-trees-in-practice lending-club-data csv.zip (18.53 MB)
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01 decision-trees-in-practice lending-club-data gl.zip (19.38 MB)
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01 decision-trees-in-practice lending-club-data sframe.zip (19.38 MB)
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01 decision-trees-in-practice module-6-assignment-train-idx json.zip (55.12 KB)
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01 decision-trees-in-practice module-6-assignment-validation-idx json.zip (18.93 KB)
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02 challenge-of-missing-data.mp4 (12.63 MB)
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03 strategy-1-purification-by-skipping-missing-data.mp4 (12.92 MB)
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04 strategy-2-purification-by-imputing-missing-data.mp4 (15.88 MB)
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02 feature-split-selection-with-missing-data.mp4 (16.06 MB)
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01 recap-of-handling-missing-data.mp4 (6.18 MB)
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02 the-boosting-question.mp4 (13.24 MB)
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03 ensemble-classifiers.mp4 (17.52 MB)
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04 boosting.mp4 (21.42 MB)
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01 adaboost-overview.mp4 (7.75 MB)
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02 weighted-error.mp4 (13.15 MB)
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03 computing-coefficient-of-each-ensemble-component.mp4 (12.1 MB)
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04 reweighing-data-to-focus-on-mistakes.mp4 (11.78 MB)
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05 normalizing-weights.mp4 (6.74 MB)
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01 example-of-adaboost-in-action.mp4 (11.87 MB)
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02 learning-boosted-decision-stumps-with-adaboost.mp4 (13.99 MB)
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01 exploring-ensemble-methods CLA08-NB01 ipynb.zip (7.11 KB)
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01 exploring-ensemble-methods lending-club-data csv.zip (18.53 MB)
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01 exploring-ensemble-methods lending-club-data gl.zip (19.38 MB)
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01 exploring-ensemble-methods lending-club-data sframe.zip (19.38 MB)
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01 exploring-ensemble-methods module-8-assignment-1-train-idx json.zip (55.03 KB)
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01 exploring-ensemble-methods module-8-assignment-1-validation-idx json.zip (18.93 KB)
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01 the-boosting-theorem.mp4 (10.85 MB)
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02 overfitting-in-boosting.mp4 (14.59 MB)
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01 ensemble-methods-impact-of-boosting-quick-recap.mp4 (16.2 MB)
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01 boosting-a-decision-stump CLA08-NB02 ipynb.zip (10.2 KB)
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01 boosting-a-decision-stump lending-club-data csv.zip (18.53 MB)
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01 boosting-a-decision-stump lending-club-data gl.zip (19.38 MB)
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01 boosting-a-decision-stump lending-club-data sframe.zip (19.38 MB)
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01 boosting-a-decision-stump module-8-assignment-2-test-idx json.zip (18.92 KB)
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01 boosting-a-decision-stump module-8-assignment-2-train-idx json.zip (55.12 KB)
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01 boosting-a-decision-stump module-8-boosting-assignment-2-blank ipynb.zip (9.76 KB)
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02 case-study-where-accuracy-is-not-best-metric-for-classification.mp4 (12.61 MB)
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03 what-is-good-performance-for-a-classifier.mp4 (13.81 MB)
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01 precision-fraction-of-positive-predictions-that-are-actually-positive.mp4 (15.57 MB)
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02 recall-fraction-of-positive-data-predicted-to-be-positive.mp4 (10.56 MB)
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01 precision-recall-extremes.mp4 (8.63 MB)
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02 trading-off-precision-and-recall.mp4 (13.2 MB)
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03 precision-recall-curve.mp4 (15.66 MB)
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01 recap-of-precision-recall.mp4 (5.11 MB)
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01 exploring-precision-and-recall amazon baby csv.zip (28.67 MB)
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01 exploring-precision-and-recall amazon baby gl.zip (40.34 MB)
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01 exploring-precision-and-recall amazon baby sframe.zip (40.33 MB)
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01 exploring-precision-and-recall CLA09-NB01 ipynb.zip (6.34 KB)
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01 exploring-precision-and-recall module-9-assignment-test-idx json.zip (46.63 KB)
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01 exploring-precision-and-recall module-9-assignment-train-idx json.zip (163.92 KB)
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02 gradient-ascent-won-t-scale-to-todays-huge-datasets.mp4 (11.97 MB)
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03 timeline-of-scalable-machine-learning-stochastic-gradient.mp4 (12.07 MB)
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01 why-gradient-ascent-won-t-scale.mp4 (8.91 MB)
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02 stochastic-gradient-learning-one-data-point-at-a-time.mp4 (8.82 MB)
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03 comparing-gradient-to-stochastic-gradient.mp4 (11.1 MB)
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01 why-would-stochastic-gradient-ever-work.mp4 (10.36 MB)
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02 convergence-paths.mp4 (7.09 MB)
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01 shuffle-data-before-running-stochastic-gradient.mp4 (5.89 MB)
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02 choosing-step-size.mp4 (9.64 MB)
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03 don-t-trust-last-coefficients.mp4 (6.02 MB)
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04 optional-learning-from-batches-of-data.mp4 (11.99 MB)
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05 optional-measuring-convergence.mp4 (12.5 MB)
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06 optional-adding-regularization.mp4 (10.85 MB)
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01 the-online-learning-task.mp4 (10.21 MB)
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03 welcome-and-introduction-to-clustering-and-retrieval-tasks.mp4 (19.84 MB)
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04 course-overview.mp4 (10.58 MB)
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05 module-by-module-topics-covered.mp4 (28.97 MB)
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06 assumed-background.mp4 (19.43 MB)
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02 retrieval-as-k-nearest-neighbor-search.mp4 (9.38 MB)
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03 1-nn-algorithm.mp4 (6.79 MB)
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04 k-nn-algorithm.mp4 (17.38 MB)
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01 document-representation.mp4 (14.08 MB)
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04 distance-metrics-cosine-similarity.mp4 (20.12 MB)
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01 choosing-features-and-metrics-for-nearest-neighbor-search CLU02-NB01 ipynb.zip (7.61 KB)
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01 choosing-features-and-metrics-for-nearest-neighbor-search people wiki csv.zip (39.86 MB)
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01 choosing-features-and-metrics-for-nearest-neighbor-search people wiki gl.zip (55.57 MB)
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01 choosing-features-and-metrics-for-nearest-neighbor-search people wiki sframe.zip (56.24 MB)
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01 complexity-of-brute-force-search.mp4 (5.92 MB)
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02 kd-tree-representation.mp4 (22.57 MB)
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03 nn-search-with-kd-trees.mp4 (16.25 MB)
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04 complexity-of-nn-search-with-kd-trees.mp4 (13.51 MB)
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05 visualizing-scaling-behavior-of-kd-trees.mp4 (10.15 MB)
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06 approximate-k-nn-search-using-kd-trees.mp4 (22.95 MB)
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01 limitations-of-kd-trees.mp4 (11.58 MB)
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02 lsh-as-an-alternative-to-kd-trees.mp4 (12.85 MB)
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03 using-random-lines-to-partition-points.mp4 (17.06 MB)
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04 defining-more-bins.mp4 (10.54 MB)
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05 searching-neighboring-bins.mp4 (22.02 MB)
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06 lsh-in-higher-dimensions.mp4 (9.9 MB)
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01 implementing-locality-sensitive-hashing-from-scratch CLU02-NB02 ipynb.zip (10.09 KB)
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01 implementing-locality-sensitive-hashing-from-scratch people wiki csv.zip (39.86 MB)
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01 implementing-locality-sensitive-hashing-from-scratch people wiki gl.zip (55.57 MB)
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01 implementing-locality-sensitive-hashing-from-scratch people wiki sframe.zip (56.24 MB)
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01 implementing-locality-sensitive-hashing-from-scratch people wiki tf idf npz.zip (50.92 MB)
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01 a-brief-recap.mp4 (6.37 MB)
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02 the-goal-of-clustering.mp4 (10.66 MB)
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03 an-unsupervised-task.mp4 (16.48 MB)
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04 hope-for-unsupervised-learning-and-some-challenge-cases.mp4 (11.58 MB)
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01 the-k-means-algorithm.mp4 (17.25 MB)
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02 k-means-as-coordinate-descent.mp4 (17.76 MB)
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03 smart-initialization-via-k-means.mp4 (12.09 MB)
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04 assessing-the-quality-and-choosing-the-number-of-clusters.mp4 (21.18 MB)
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01 clustering-text-data-with-k-means CLU03-NB01 ipynb.zip (13.96 KB)
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01 clustering-text-data-with-k-means kmeans-arrays npz.zip (47.62 MB)
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01 clustering-text-data-with-k-means people wiki csv.zip (39.86 MB)
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01 clustering-text-data-with-k-means people wiki gl.zip (55.57 MB)
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01 clustering-text-data-with-k-means people wiki sframe.zip (56.24 MB)
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01 clustering-text-data-with-k-means people wiki map index to word gl.zip (3.7 MB)
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01 clustering-text-data-with-k-means people wiki map index to word json.zip (5.04 MB)
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01 clustering-text-data-with-k-means people wiki tf idf npz.zip (50.92 MB)
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01 motivating-mapreduce.mp4 (20.39 MB)
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02 the-general-mapreduce-abstraction.mp4 (12.11 MB)
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03 mapreduce-execution-overview-and-combiners.mp4 (14.42 MB)
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04 mapreduce-for-k-means.mp4 (18.54 MB)
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01 other-applications-of-clustering.mp4 (21.55 MB)
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03 aggregating-over-unknown-classes-in-an-image-dataset.mp4 (21.56 MB)
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04 univariate-gaussian-distributions.mp4 (9.02 MB)
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01 computing-soft-assignments-from-known-cluster-parameters.mp4 (21.76 MB)
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02 optional-responsibilities-as-bayes-rule.mp4 (14.41 MB)
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04 estimating-cluster-parameters-from-soft-assignments.mp4 (21.4 MB)
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01 em-iterates-in-equations-and-pictures.mp4 (16.69 MB)
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02 convergence-initialization-and-overfitting-of-em.mp4 (29.91 MB)
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03 relationship-to-k-means.mp4 (10.37 MB)
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01 a-brief-recap.mp4 (4.42 MB)
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01 implementing-em-for-gaussian-mixtures CLU04-NB01 ipynb.zip (11.17 KB)
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01 implementing-em-for-gaussian-mixtures images sf.zip (11.18 MB)
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01 implementing-em-for-gaussian-mixtures images.zip (13.79 MB)
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01 clustering-text-data-with-gaussian-mixtures 4 map index to word gl.zip (623.64 KB)
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01 clustering-text-data-with-gaussian-mixtures 4 map index to word json.zip (834.29 KB)
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01 clustering-text-data-with-gaussian-mixtures 4 tf idf npz.zip (3.24 MB)
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01 clustering-text-data-with-gaussian-mixtures CLU04-NB02 ipynb.zip (5.51 KB)
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01 clustering-text-data-with-gaussian-mixtures em utilities py.zip (2.79 KB)
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01 clustering-text-data-with-gaussian-mixtures people wiki csv.zip (39.86 MB)
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01 clustering-text-data-with-gaussian-mixtures people wiki gl.zip (55.57 MB)
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01 clustering-text-data-with-gaussian-mixtures people wiki sframe.zip (56.24 MB)
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05 goal-of-lda-inference.mp4 (18.22 MB)
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01 what-is-collapsed-gibbs-sampling.mp4 (11.71 MB)
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01 a-brief-recap.mp4 (5.05 MB)
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04 the-dendrogram.mp4 (15.35 MB)
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01 modeling-text-data-with-a-hierarchy-of-clusters em utilities py.zip (2.79 KB)
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01 modeling-text-data-with-a-hierarchy-of-clusters people wiki csv.zip (39.86 MB)
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01 modeling-text-data-with-a-hierarchy-of-clusters people wiki gl.zip (55.57 MB)
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01 what-we-didn-t-cover.mp4 (8.6 MB)
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02 thank-you.mp4 (7.11 MB)
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03 welcome-to-this-course-and-specialization.mp4 (3.32 MB)
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04 who-we-are.mp4 (28.43 MB)
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05 machine-learning-is-changing-the-world.mp4 (16.31 MB)
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06 why-a-case-study-approach.mp4 (29.06 MB)
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07 specialization-overview.mp4 (27.28 MB)
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01 how-we-got-into-ml.mp4 (19.12 MB)
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02 who-is-this-specialization-for.mp4 (14.35 MB)
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03 what-you-ll-be-able-to-do.mp4 (4.45 MB)
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04 the-capstone-and-an-example-intelligent-application.mp4 (22.15 MB)
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05 the-future-of-intelligent-applications.mp4 (12.79 MB)
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02 starting-a-jupyter-notebook.mp4 (13.77 MB)
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03 creating-variables-in-python.mp4 (17.69 MB)
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04 conditional-statements-and-loops-in-python.mp4 (20.14 MB)
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05 creating-functions-and-lambdas-in-python.mp4 (9.06 MB)
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02 starting-turi-create-loading-an-sframe.mp4 (11.53 MB)
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03 canvas-for-data-visualization.mp4 (9.76 MB)
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04 interacting-with-columns-of-an-sframe.mp4 (10.46 MB)
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05 using-apply-for-data-transformation.mp4 (12.46 MB)
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01 download-wiki-people-data people wiki sframe.zip (56.24 MB)
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02 predicting-house-prices-a-case-study-in-regression.mp4 (3.83 MB)
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03 what-is-the-goal-and-how-might-you-naively-address-it.mp4 (10.06 MB)
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04 linear-regression-a-model-based-approach.mp4 (12.72 MB)
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05 adding-higher-order-effects.mp4 (10.41 MB)
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01 evaluating-overfitting-via-training-test-split.mp4 (15.44 MB)
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02 training-test-curves.mp4 (9.46 MB)
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03 adding-other-features.mp4 (6.66 MB)
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04 other-regression-examples.mp4 (13.48 MB)
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01 regression-ml-block-diagram.mp4 (15.47 MB)
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02 loading-exploring-house-sale-data.mp4 (18.41 MB)
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03 splitting-the-data-into-training-and-test-sets.mp4 (6.17 MB)
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05 evaluating-error-rmse-of-the-simple-model.mp4 (6.15 MB)
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06 visualizing-predictions-of-simple-model-with-matplotlib.mp4 (11.87 MB)
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07 inspecting-the-model-coefficients-learned.mp4 (3.39 MB)
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08 exploring-other-features-of-the-data.mp4 (13.66 MB)
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09 learning-a-model-to-predict-house-prices-from-more-features.mp4 (7.73 MB)
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10 applying-learned-models-to-predict-price-of-an-average-house.mp4 (12.65 MB)
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11 applying-learned-models-to-predict-price-of-two-fancy-houses.mp4 (22.37 MB)
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01 predicting-house-prices-assignment FND02-NB01 ipynb.zip (67.26 KB)
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01 predicting-house-prices-assignment home data sframe.zip (908.17 KB)
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01 predicting-house-prices-assignment house images.zip (4.66 MB)
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02 analyzing-the-sentiment-of-reviews-a-case-study-in-classification.mp4 (2.85 MB)
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03 what-is-an-intelligent-restaurant-review-system.mp4 (18.51 MB)
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04 examples-of-classification-tasks.mp4 (23.02 MB)
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05 linear-classifiers.mp4 (18.54 MB)
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06 decision-boundaries.mp4 (16 MB)
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01 training-and-evaluating-a-classifier.mp4 (12.95 MB)
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02 whats-a-good-accuracy.mp4 (15.81 MB)
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03 false-positives-false-negatives-and-confusion-matrices.mp4 (16.92 MB)
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04 learning-curves.mp4 (22.21 MB)
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05 class-probabilities.mp4 (8.66 MB)
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01 classification-ml-block-diagram.mp4 (10.66 MB)
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02 loading-exploring-product-review-data.mp4 (8 MB)
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03 creating-the-word-count-vector.mp4 (6.19 MB)
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04 exploring-the-most-popular-product.mp4 (12.11 MB)
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05 defining-which-reviews-have-positive-or-negative-sentiment.mp4 (11.62 MB)
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06 training-a-sentiment-classifier.mp4 (9.07 MB)
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07 evaluating-a-classifier-the-roc-curve.mp4 (11.89 MB)
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08 applying-model-to-find-most-positive-negative-reviews-for-a-product.mp4 (13.19 MB)
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09 exploring-the-most-positive-negative-aspects-of-a-product.mp4 (15.78 MB)
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01 analyzing-product-sentiment-assignment amazon baby sframe.zip (40.33 MB)
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01 analyzing-product-sentiment-assignment FND03-NB01 ipynb.zip (13.08 KB)
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06 calculating-tf-idf-vectors.mp4 (14.52 MB)
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01 clustering-documents-task-overview.mp4 (9.84 MB)
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02 clustering-documents-an-unsupervised-learning-task.mp4 (11.27 MB)
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03 k-means-a-clustering-algorithm.mp4 (9.48 MB)
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04 other-examples-of-clustering.mp4 (19.77 MB)
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01 clustering-and-similarity-ml-block-diagram.mp4 (18.91 MB)
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02 loading-exploring-wikipedia-data.mp4 (17.54 MB)
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03 exploring-word-counts.mp4 (18.75 MB)
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04 computing-exploring-tf-idfs.mp4 (18.35 MB)
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05 computing-distances-between-wikipedia-articles.mp4 (15.38 MB)
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07 examples-of-document-retrieval-in-action.mp4 (12.94 MB)
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01 retrieving-wikipedia-articles-assignment FND04-NB01 ipynb.zip (11.61 KB)
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01 retrieving-wikipedia-articles-assignment people wiki sframe.zip (56.24 MB)
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02 recommender-systems-overview.mp4 (4.62 MB)
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03 where-we-see-recommender-systems-in-action.mp4 (26.7 MB)
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04 building-a-recommender-system-via-classification.mp4 (13.42 MB)
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01 collaborative-filtering-people-who-bought-this-also-bought.mp4 (15.26 MB)
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02 effect-of-popular-items.mp4 (8.05 MB)
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01 the-matrix-completion-task.mp4 (14.78 MB)
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02 recommendations-from-known-user-item-features.mp4 (14.18 MB)
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03 predictions-in-matrix-form.mp4 (7.45 MB)
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04 discovering-hidden-structure-by-matrix-factorization.mp4 (17.22 MB)
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05 bringing-it-all-together-featurized-matrix-factorization.mp4 (11.26 MB)
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01 a-performance-metric-for-recommender-systems.mp4 (16.52 MB)
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02 optimal-recommenders.mp4 (5.88 MB)
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03 precision-recall-curves.mp4 (16.28 MB)
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01 recommender-systems-ml-block-diagram.mp4 (12.28 MB)
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01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along FND05-NB01 ipynb.zip (15.14 KB)
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01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along song data sframe.zip (47.97 MB)
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02 loading-and-exploring-song-data.mp4 (14.79 MB)
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03 creating-evaluating-a-popularity-based-song-recommender.mp4 (13.15 MB)
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04 creating-evaluating-a-personalized-song-recommender.mp4 (16.4 MB)
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05 using-precision-recall-to-compare-recommender-models.mp4 (11.25 MB)
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01 recommending-songs-assignment FND05-NB01 ipynb.zip (15.14 KB)
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01 recommending-songs-assignment song data sframe.zip (47.97 MB)
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02 searching-for-images-a-case-study-in-deep-learning.mp4 (1.68 MB)
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03 what-is-a-visual-product-recommender.mp4 (16.65 MB)
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04 learning-very-non-linear-features-with-neural-networks.mp4 (32.85 MB)
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01 application-of-deep-learning-to-computer-vision.mp4 (19.31 MB)
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02 deep-learning-performance.mp4 (13.24 MB)
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03 demo-of-deep-learning-model-on-imagenet-data.mp4 (7.87 MB)
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04 other-examples-of-deep-learning-in-computer-vision.mp4 (6.71 MB)
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05 challenges-of-deep-learning.mp4 (11.07 MB)
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06 deep-features.mp4 (24.39 MB)
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01 deep-learning-ml-block-diagram.mp4 (11.14 MB)
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02 loading-image-data.mp4 (9.42 MB)
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02 loading-image-data.mp4 (7.42 MB)
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05 querying-for-the-most-similar-images-for-car-image.mp4 (4.64 MB)
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01 deep-features-for-image-retrieval-assignment image test data.zip (50.92 MB)
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01 deep-features-for-image-retrieval-assignment image train data.zip (25.35 MB)
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02 you-ve-made-it.mp4 (2.82 MB)
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03 deploying-an-ml-service.mp4 (15.11 MB)
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04 what-happens-after-deployment.mp4 (29.95 MB)
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01 open-challenges-in-ml.mp4 (33.33 MB)
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02 where-is-ml-going.mp4 (37.8 MB)
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03 whats-ahead-in-the-specialization.mp4 (22.19 MB)
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04 thank-you.mp4 (8.23 MB)
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03 welcome.mp4 (4.87 MB)
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04 what-is-the-course-about.mp4 (11.19 MB)
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05 outlining-the-first-half-of-the-course.mp4 (14.84 MB)
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06 outlining-the-second-half-of-the-course.mp4 (16.16 MB)
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07 assumed-background.mp4 (11.85 MB)
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02 a-case-study-in-predicting-house-prices.mp4 (3.81 MB)
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03 regression-fundamentals-data-model.mp4 (22.35 MB)
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04 regression-fundamentals-the-task.mp4 (7.33 MB)
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05 regression-ml-block-diagram.mp4 (11.74 MB)
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01 the-simple-linear-regression-model.mp4 (7.65 MB)
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02 the-cost-of-using-a-given-line.mp4 (17.45 MB)
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03 using-the-fitted-line.mp4 (16.62 MB)
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04 interpreting-the-fitted-line.mp4 (15.17 MB)
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01 defining-our-least-squares-optimization-objective.mp4 (10.19 MB)
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02 finding-maxima-or-minima-analytically.mp4 (16.85 MB)
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03 maximizing-a-1d-function-a-worked-example.mp4 (7.33 MB)
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04 finding-the-max-via-hill-climbing.mp4 (15.69 MB)
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05 finding-the-min-via-hill-descent.mp4 (8.54 MB)
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06 choosing-stepsize-and-convergence-criteria.mp4 (14.71 MB)
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01 gradients-derivatives-in-multiple-dimensions.mp4 (14.01 MB)
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02 gradient-descent-multidimensional-hill-descent.mp4 (16.87 MB)
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01 computing-the-gradient-of-rss.mp4 (17.03 MB)
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02 approach-1-closed-form-solution.mp4 (14.07 MB)
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04 approach-2-gradient-descent.mp4 (18.36 MB)
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06 comparing-the-approaches.mp4 (6.22 MB)
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01 download-notebooks-to-follow-along PhillyCrime ipynb.zip (44.94 KB)
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02 influence-of-high-leverage-points-exploring-the-data.mp4 (11.92 MB)
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03 influence-of-high-leverage-points-removing-center-city.mp4 (19.52 MB)
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04 influence-of-high-leverage-points-removing-high-end-towns.mp4 (9.33 MB)
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05 asymmetric-cost-functions.mp4 (9.4 MB)
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06 a-brief-recap.mp4 (4.48 MB)
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01 fitting-a-simple-linear-regression-model-on-housing-data home data sframe.zip (908.17 KB)
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01 fitting-a-simple-linear-regression-model-on-housing-data kc house data csv.zip (780.66 KB)
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01 fitting-a-simple-linear-regression-model-on-housing-data kc house test data csv.zip (154.8 KB)
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01 fitting-a-simple-linear-regression-model-on-housing-data kc house train data csv.zip (628.45 KB)
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01 fitting-a-simple-linear-regression-model-on-housing-data REG01-NB01 ipynb.zip (4.07 KB)
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02 multiple-regression-intro.mp4 (1.21 MB)
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03 polynomial-regression.mp4 (11.56 MB)
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04 modeling-seasonality.mp4 (30.01 MB)
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05 where-we-see-seasonality.mp4 (11.26 MB)
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06 regression-with-general-features-of-1-input.mp4 (8.6 MB)
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01 motivating-the-use-of-multiple-inputs.mp4 (14.17 MB)
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02 defining-notation.mp4 (11.87 MB)
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03 regression-with-features-of-multiple-inputs.mp4 (12.6 MB)
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04 interpreting-the-multiple-regression-fit.mp4 (24.67 MB)
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02 rewriting-the-single-observation-model-in-vector-notation.mp4 (15.44 MB)
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03 rewriting-the-model-for-all-observations-in-matrix-notation.mp4 (10.19 MB)
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04 computing-the-cost-of-a-d-dimensional-curve.mp4 (21.24 MB)
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01 computing-the-gradient-of-rss.mp4 (7.25 MB)
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02 approach-1-closed-form-solution.mp4 (9.99 MB)
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03 discussing-the-closed-form-solution.mp4 (10.85 MB)
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04 approach-2-gradient-descent.mp4 (5.36 MB)
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05 feature-by-feature-update.mp4 (18.98 MB)
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06 algorithmic-summary-of-gradient-descent-approach.mp4 (10.97 MB)
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01 a-brief-recap.mp4 (3.91 MB)
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01 exploring-different-multiple-regression-models-for-house-price-prediction home data sframe.zip (908.17 KB)
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01 exploring-different-multiple-regression-models-for-house-price-prediction kc house data csv.zip (780.66 KB)
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01 exploring-different-multiple-regression-models-for-house-price-prediction REG02-NB01 ipynb.zip (3.44 KB)
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01 numpy-tutorial numpy-tutorial ipynb.zip (2.95 KB)
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02 implementing-gradient-descent-for-multiple-regression home data sframe.zip (908.17 KB)
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02 implementing-gradient-descent-for-multiple-regression kc house data csv.zip (780.66 KB)
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02 implementing-gradient-descent-for-multiple-regression kc house test data csv.zip (154.8 KB)
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02 implementing-gradient-descent-for-multiple-regression kc house train data csv.zip (628.45 KB)
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02 implementing-gradient-descent-for-multiple-regression REG02-NB02 ipynb.zip (5.65 KB)
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02 assessing-performance-intro.mp4 (1.77 MB)
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03 what-do-we-mean-by-loss.mp4 (13.85 MB)
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01 training-error-assessing-loss-on-the-training-set.mp4 (20.37 MB)
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02 generalization-error-what-we-really-want.mp4 (21.76 MB)
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03 test-error-what-we-can-actually-compute.mp4 (12.93 MB)
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04 defining-overfitting.mp4 (6.1 MB)
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05 training-test-split.mp4 (6.1 MB)
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01 irreducible-error-and-bias.mp4 (20.89 MB)
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02 variance-and-the-bias-variance-tradeoff.mp4 (18.96 MB)
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03 error-vs-amount-of-data.mp4 (14.68 MB)
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01 training-validation-test-split-for-model-selection-fitting-and-assessment.mp4 (25.37 MB)
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02 a-brief-recap.mp4 (5.01 MB)
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01 polynomial-regression home data sframe.zip (908.17 KB)
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01 polynomial-regression kc house data csv.zip (780.66 KB)
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01 polynomial-regression numpy-tutorial-py3 ipynb.zip (2.92 KB)
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01 polynomial-regression REG03-NB01 ipynb.zip (4.2 KB)
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01 polynomial-regression wk3 kc house set 1 data csv.zip (158.64 KB)
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01 polynomial-regression wk3 kc house set 2 data csv.zip (157.64 KB)
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01 polynomial-regression wk3 kc house set 3 data csv.zip (158.66 KB)
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01 polynomial-regression wk3 kc house set 4 data csv.zip (158.3 KB)
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01 polynomial-regression wk3 kc house test data csv.zip (81.7 KB)
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01 polynomial-regression wk3 kc house train data csv.zip (354.68 KB)
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01 polynomial-regression wk3 kc house valid data csv.zip (349.96 KB)
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02 symptoms-of-overfitting-in-polynomial-regression.mp4 (6.91 MB)
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03 download-the-notebook-and-follow-along Overfitting Demo Ridge Lasso ipynb.zip (198.44 KB)
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04 overfitting-demo.mp4 (16.94 MB)
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05 overfitting-for-more-general-multiple-regression-models.mp4 (11.33 MB)
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01 balancing-fit-and-magnitude-of-coefficients.mp4 (18.11 MB)
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02 the-resulting-ridge-objective-and-its-extreme-solutions.mp4 (13.61 MB)
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03 how-ridge-regression-balances-bias-and-variance.mp4 (5.33 MB)
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04 download-the-notebook-and-follow-along Overfitting Demo Ridge Lasso ipynb.zip (198.44 KB)
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05 ridge-regression-demo.mp4 (24.95 MB)
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06 the-ridge-coefficient-path.mp4 (9.57 MB)
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01 computing-the-gradient-of-the-ridge-objective.mp4 (14.78 MB)
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02 approach-1-closed-form-solution.mp4 (13.31 MB)
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03 discussing-the-closed-form-solution.mp4 (13.45 MB)
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04 approach-2-gradient-descent.mp4 (22.08 MB)
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01 selecting-tuning-parameters-via-cross-validation.mp4 (12.39 MB)
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02 k-fold-cross-validation.mp4 (17.01 MB)
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03 how-to-handle-the-intercept.mp4 (18.6 MB)
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04 a-brief-recap.mp4 (5.24 MB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression home data sframe.zip (908.17 KB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression kc house data csv.zip (780.66 KB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression numpy-tutorial-py3 ipynb.zip (2.92 KB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression REG04-NB01 ipynb.zip (5.9 KB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house set 1 data csv.zip (158.64 KB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house set 2 data csv.zip (157.64 KB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house set 3 data csv.zip (158.66 KB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house set 4 data csv.zip (158.3 KB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house test data csv.zip (81.7 KB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house train data csv.zip (354.68 KB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house train valid shuffled csv.zip (609.97 KB)
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01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house valid data csv.zip (349.96 KB)
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01 implementing-ridge-regression-via-gradient-descent home data sframe.zip (908.17 KB)
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01 implementing-ridge-regression-via-gradient-descent kc house data csv.zip (780.66 KB)
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01 implementing-ridge-regression-via-gradient-descent kc house test data csv.zip (154.8 KB)
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01 implementing-ridge-regression-via-gradient-descent kc house train data csv.zip (628.45 KB)
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01 implementing-ridge-regression-via-gradient-descent numpy-tutorial-py3 ipynb.zip (2.92 KB)
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01 implementing-ridge-regression-via-gradient-descent REG04-NB02 ipynb.zip (4.71 KB)
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02 the-feature-selection-task.mp4 (11.53 MB)
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03 all-subsets.mp4 (13.65 MB)
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04 complexity-of-all-subsets.mp4 (6.84 MB)
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05 greedy-algorithms.mp4 (18.66 MB)
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06 complexity-of-the-greedy-forward-stepwise-algorithm.mp4 (8.4 MB)
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01 can-we-use-regularization-for-feature-selection.mp4 (11.56 MB)
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02 thresholding-ridge-coefficients.mp4 (13.86 MB)
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03 the-lasso-objective-and-its-coefficient-path.mp4 (15.55 MB)
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01 visualizing-the-ridge-cost.mp4 (17.83 MB)
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02 visualizing-the-ridge-solution.mp4 (14.71 MB)
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03 visualizing-the-lasso-cost-and-solution.mp4 (18.4 MB)
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05 lasso-demo.mp4 (12.17 MB)
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01 what-makes-the-lasso-objective-different.mp4 (8.39 MB)
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02 coordinate-descent.mp4 (15.11 MB)
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03 normalizing-features.mp4 (9.35 MB)
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04 coordinate-descent-for-least-squares-regression-normalized-features.mp4 (20.81 MB)
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01 coordinate-descent-for-lasso-normalized-features.mp4 (12.61 MB)
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02 assessing-convergence-and-other-lasso-solvers.mp4 (9.33 MB)
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03 coordinate-descent-for-lasso-unnormalized-features.mp4 (5.91 MB)
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01 deriving-the-lasso-coordinate-descent-update.mp4 (42.7 MB)
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01 choosing-the-penalty-strength-and-other-practical-issues-with-lasso.mp4 (19.38 MB)
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02 a-brief-recap.mp4 (11.18 MB)
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01 using-lasso-to-select-features home data sframe.zip (908.17 KB)
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01 using-lasso-to-select-features kc house data csv.zip (780.66 KB)
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01 using-lasso-to-select-features numpy-tutorial-py3 ipynb.zip (2.92 KB)
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01 using-lasso-to-select-features REG05-NB01 ipynb.zip (3.94 KB)
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01 using-lasso-to-select-features wk3 kc house test data csv.zip (81.7 KB)
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01 using-lasso-to-select-features wk3 kc house train data csv.zip (354.68 KB)
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01 using-lasso-to-select-features wk3 kc house valid data csv.zip (349.96 KB)
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01 implementing-lasso-using-coordinate-descent home data sframe.zip (908.17 KB)
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01 implementing-lasso-using-coordinate-descent kc house data csv.zip (780.66 KB)
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01 implementing-lasso-using-coordinate-descent kc house test data csv.zip (154.8 KB)
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01 implementing-lasso-using-coordinate-descent kc house train data csv.zip (628.45 KB)
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01 implementing-lasso-using-coordinate-descent numpy-tutorial-py3 ipynb.zip (2.92 KB)
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01 implementing-lasso-using-coordinate-descent REG05-NB02 ipynb.zip (5.66 KB)
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02 limitations-of-parametric-regression.mp4 (11.29 MB)
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01 1-nearest-neighbor-regression-approach.mp4 (21.48 MB)
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02 distance-metrics.mp4 (14.18 MB)
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03 1-nearest-neighbor-algorithm.mp4 (10.84 MB)
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01 k-nearest-neighbors-regression.mp4 (19.3 MB)
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02 k-nearest-neighbors-in-practice.mp4 (11.67 MB)
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03 weighted-k-nearest-neighbors.mp4 (13.81 MB)
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01 from-weighted-k-nn-to-kernel-regression.mp4 (18.56 MB)
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02 global-fits-of-parametric-models-vs-local-fits-of-kernel-regression.mp4 (20.08 MB)
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01 performance-of-nn-as-amount-of-data-grows.mp4 (21.87 MB)
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02 issues-with-high-dimensions-data-scarcity-and-computational-complexity.mp4 (9.8 MB)
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03 k-nn-for-classification.mp4 (6.58 MB)
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04 a-brief-recap.mp4 (4.7 MB)
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01 predicting-house-prices-using-k-nearest-neighbors-regression REG06-NB01 ipynb.zip (5.29 KB)
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02 simple-and-multiple-regression.mp4 (13.05 MB)
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03 assessing-performance-and-ridge-regression.mp4 (20.72 MB)
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04 feature-selection-lasso-and-nearest-neighbor-regression.mp4 (12.06 MB)
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01 what-we-covered-and-what-we-didn-t-cover.mp4 (16.55 MB)
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02 thank-you.mp4 (5.78 MB)
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01 welcome.mp4 (8.27 MB)
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01 what-is-clustering.mp4 (8.82 MB)
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02 k-means-intuition.mp4 (12.36 MB)
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03 k-means-algorithm.mp4 (19.76 MB)
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04 optimization-objective.mp4 (29.51 MB)
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05 initializing-k-means.mp4 (17.84 MB)
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06 choosing-the-number-of-clusters.mp4 (16.85 MB)
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01 finding-unusual-events.mp4 (26.28 MB)
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02 gaussian-normal-distribution.mp4 (20.88 MB)
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03 anomaly-detection-algorithm.mp4 (20.32 MB)
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04 developing-and-evaluating-an-anomaly-detection-system.mp4 (23.9 MB)
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05 anomaly-detection-vs-supervised-learning.mp4 (20.31 MB)
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06 choosing-what-features-to-use.mp4 (30.87 MB)
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01 making-recommendations.mp4 (20.44 MB)
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02 using-per-item-features.mp4 (23.49 MB)
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03 collaborative-filtering-algorithm.mp4 (31.03 MB)
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04 binary-labels-favs-likes-and-clicks.mp4 (19.84 MB)
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01 mean-normalization.mp4 (18.9 MB)
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03 finding-related-items.mp4 (16.62 MB)
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01 collaborative-filtering-vs-content-based-filtering.mp4 (19.97 MB)
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02 deep-learning-for-content-based-filtering.mp4 (24.34 MB)
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03 recommending-from-a-large-catalogue.mp4 (17.98 MB)
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04 ethical-use-of-recommender-systems.mp4 (24.83 MB)
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05 tensorflow-implementation-of-content-based-filtering.mp4 (12.94 MB)
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01 reducing-the-number-of-features-optional.mp4 (26.7 MB)
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02 pca-algorithm-optional.mp4 (28.01 MB)
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03 pca-in-code-optional.mp4 (17.8 MB)
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01 what-is-reinforcement-learning.mp4 (30.97 MB)
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02 mars-rover-example.mp4 (12.65 MB)
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03 the-return-in-reinforcement-learning.mp4 (29.01 MB)
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05 review-of-key-concepts.mp4 (11.39 MB)
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01 state-action-value-function-definition.mp4 (19.84 MB)
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02 state-action-value-function-example.mp4 (14.64 MB)
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03 bellman-equation.mp4 (26.66 MB)
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04 random-stochastic-environment-optional.mp4 (19.27 MB)
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01 example-of-continuous-state-space-applications.mp4 (27.05 MB)
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02 lunar-lander.mp4 (10.37 MB)
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03 learning-the-state-value-function.mp4 (31.14 MB)
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05 algorithm-refinement-greedy-policy.mp4 (25.27 MB)
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07 the-state-of-reinforcement-learning.mp4 (7.86 MB)
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01 summary-and-thank-you.mp4 (13.94 MB)
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