Coursera - Machine Learning on Google Cloud 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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03 model-selection-and-training-cross-validation-test-sets.mp4 (29.62 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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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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04 making-decisions-policies-in-reinforcement-learning.mp4 (5.81 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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04 algorithm-refinement-improved-neural-network-architecture.mp4 (7.79 MB)
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05 algorithm-refinement-greedy-policy.mp4 (25.27 MB)
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06 algorithm-refinement-mini-batch-and-soft-updates-optional.mp4 (25.55 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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01 andrew-ng-and-chelsea-finn-on-ai-and-robotics.mp4 (230.66 MB)
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