Coursera Machine Learning Specialization

/.../01_neural-networks-intuition/

01_welcome.en.srt

01_welcome.en.txt

01_welcome.mp4

02_neurons-and-the-brain.en.srt

02_neurons-and-the-brain.en.txt

02_neurons-and-the-brain.mp4

03_demand-prediction.en.srt

03_demand-prediction.en.txt

03_demand-prediction.mp4

04_example-recognizing-images.en.srt

04_example-recognizing-images.en.txt

04_example-recognizing-images.mp4

05_important-have-questions-issues-or-ideas-join-our-forum_instructions.html

/.../02_neural-network-model/

01_neural-network-layer.en.srt

01_neural-network-layer.en.txt

01_neural-network-layer.mp4

02_more-complex-neural-networks.en.srt

02_more-complex-neural-networks.en.txt

02_more-complex-neural-networks.mp4

03_inference-making-predictions-forward-propagation.en.srt

03_inference-making-predictions-forward-propagation.en.txt

03_inference-making-predictions-forward-propagation.mp4

/.../03_tensorflow-implementation/

01_inference-in-code.en.srt

01_inference-in-code.en.txt

01_inference-in-code.mp4

02_data-in-tensorflow.en.srt

02_data-in-tensorflow.en.txt

02_data-in-tensorflow.mp4

03_building-a-neural-network.en.srt

03_building-a-neural-network.en.txt

03_building-a-neural-network.mp4

/.../04_neural-network-implementation-in-python/

01_forward-prop-in-a-single-layer.en.srt

01_forward-prop-in-a-single-layer.en.txt

01_forward-prop-in-a-single-layer.mp4

02_general-implementation-of-forward-propagation.en.srt

02_general-implementation-of-forward-propagation.en.txt

02_general-implementation-of-forward-propagation.mp4

/.../05_speculations-on-artificial-general-intelligence-agi/

01_is-there-a-path-to-agi.en.srt

01_is-there-a-path-to-agi.en.txt

01_is-there-a-path-to-agi.mp4

/.../06_vectorization-optional/

01_how-neural-networks-are-implemented-efficiently.en.srt

01_how-neural-networks-are-implemented-efficiently.en.txt

01_how-neural-networks-are-implemented-efficiently.mp4

02_matrix-multiplication.en.srt

02_matrix-multiplication.en.txt

02_matrix-multiplication.mp4

03_matrix-multiplication-rules.en.srt

03_matrix-multiplication-rules.en.txt

03_matrix-multiplication-rules.mp4

04_matrix-multiplication-code.en.srt

04_matrix-multiplication-code.en.txt

04_matrix-multiplication-code.mp4

/.../01_neural-network-training/

01_tensorflow-implementation.en.srt

01_tensorflow-implementation.en.txt

01_tensorflow-implementation.mp4

02_training-details.en.srt

02_training-details.en.txt

02_training-details.mp4

/.../02_activation-functions/

01_alternatives-to-the-sigmoid-activation.en.srt

01_alternatives-to-the-sigmoid-activation.en.txt

01_alternatives-to-the-sigmoid-activation.mp4

02_choosing-activation-functions.en.srt

02_choosing-activation-functions.en.txt

02_choosing-activation-functions.mp4

03_why-do-we-need-activation-functions.en.srt

03_why-do-we-need-activation-functions.en.txt

03_why-do-we-need-activation-functions.mp4

/.../03_multiclass-classification/

01_multiclass.en.srt

01_multiclass.en.txt

01_multiclass.mp4

02_softmax.en.srt

02_softmax.en.txt

02_softmax.mp4

03_neural-network-with-softmax-output.en.srt

03_neural-network-with-softmax-output.en.txt

03_neural-network-with-softmax-output.mp4

04_improved-implementation-of-softmax.en.srt

04_improved-implementation-of-softmax.en.txt

04_improved-implementation-of-softmax.mp4

05_classification-with-multiple-outputs-optional.en.srt

05_classification-with-multiple-outputs-optional.en.txt

05_classification-with-multiple-outputs-optional.mp4

/.../04_additional-neural-network-concepts/

01_advanced-optimization.en.srt

01_advanced-optimization.en.txt

01_advanced-optimization.mp4

02_additional-layer-types.en.srt

02_additional-layer-types.en.txt

02_additional-layer-types.mp4

/.../05_back-propagation-optional/

01_what-is-a-derivative-optional.en.srt

01_what-is-a-derivative-optional.en.txt

01_what-is-a-derivative-optional.mp4

02_computation-graph-optional.en.srt

02_computation-graph-optional.en.txt

02_computation-graph-optional.mp4

03_larger-neural-network-example-optional.en.srt

03_larger-neural-network-example-optional.en.txt

03_larger-neural-network-example-optional.mp4

/.../01_advice-for-applying-machine-learning/

01_deciding-what-to-try-next.en.srt

01_deciding-what-to-try-next.en.txt

01_deciding-what-to-try-next.mp4

02_evaluating-a-model.en.srt

02_evaluating-a-model.en.txt

02_evaluating-a-model.mp4

03_model-selection-and-training-cross-validation-test-sets.en.srt

03_model-selection-and-training-cross-validation-test-sets.en.txt

03_model-selection-and-training-cross-validation-test-sets.mp4

/.../02_bias-and-variance/

01_diagnosing-bias-and-variance.en.srt

01_diagnosing-bias-and-variance.en.txt

01_diagnosing-bias-and-variance.mp4

02_regularization-and-bias-variance.en.srt

02_regularization-and-bias-variance.en.txt

02_regularization-and-bias-variance.mp4

03_establishing-a-baseline-level-of-performance.en.srt

03_establishing-a-baseline-level-of-performance.en.txt

03_establishing-a-baseline-level-of-performance.mp4

04_learning-curves.en.srt

04_learning-curves.en.txt

04_learning-curves.mp4

05_deciding-what-to-try-next-revisited.en.srt

05_deciding-what-to-try-next-revisited.en.txt

05_deciding-what-to-try-next-revisited.mp4

06_bias-variance-and-neural-networks.en.srt

06_bias-variance-and-neural-networks.en.txt

06_bias-variance-and-neural-networks.mp4

/.../03_machine-learning-development-process/

01_iterative-loop-of-ml-development.en.srt

01_iterative-loop-of-ml-development.en.txt

01_iterative-loop-of-ml-development.mp4

02_error-analysis.en.srt

02_error-analysis.en.txt

02_error-analysis.mp4

03_adding-data.en.srt

03_adding-data.en.txt

03_adding-data.mp4

04_transfer-learning-using-data-from-a-different-task.en.srt

04_transfer-learning-using-data-from-a-different-task.en.txt

04_transfer-learning-using-data-from-a-different-task.mp4

05_full-cycle-of-a-machine-learning-project.en.srt

05_full-cycle-of-a-machine-learning-project.en.txt

05_full-cycle-of-a-machine-learning-project.mp4

06_fairness-bias-and-ethics.en.srt

06_fairness-bias-and-ethics.en.txt

06_fairness-bias-and-ethics.mp4

/.../04_skewed-datasets-optional/

01_error-metrics-for-skewed-datasets.en.srt

01_error-metrics-for-skewed-datasets.en.txt

01_error-metrics-for-skewed-datasets.mp4

02_trading-off-precision-and-recall.en.srt

02_trading-off-precision-and-recall.en.txt

02_trading-off-precision-and-recall.mp4

/.../01_decision-trees/

01_decision-tree-model.en.srt

01_decision-tree-model.en.txt

01_decision-tree-model.mp4

02_learning-process.en.srt

02_learning-process.en.txt

02_learning-process.mp4

/.../02_decision-tree-learning/

01_measuring-purity.en.srt

01_measuring-purity.en.txt

01_measuring-purity.mp4

02_choosing-a-split-information-gain.en.srt

02_choosing-a-split-information-gain.en.txt

02_choosing-a-split-information-gain.mp4

03_putting-it-together.en.srt

03_putting-it-together.en.txt

03_putting-it-together.mp4

04_using-one-hot-encoding-of-categorical-features.en.srt

04_using-one-hot-encoding-of-categorical-features.en.txt

04_using-one-hot-encoding-of-categorical-features.mp4

05_continuous-valued-features.en.srt

05_continuous-valued-features.en.txt

05_continuous-valued-features.mp4

06_regression-trees-optional.en.srt

06_regression-trees-optional.en.txt

06_regression-trees-optional.mp4

/.../03_tree-ensembles/

01_using-multiple-decision-trees.en.srt

01_using-multiple-decision-trees.en.txt

01_using-multiple-decision-trees.mp4

02_sampling-with-replacement.en.srt

02_sampling-with-replacement.en.txt

02_sampling-with-replacement.mp4

03_random-forest-algorithm.en.srt

03_random-forest-algorithm.en.txt

03_random-forest-algorithm.mp4

04_xgboost.en.srt

04_xgboost.en.txt

04_xgboost.mp4

05_when-to-use-decision-trees.en.srt

05_when-to-use-decision-trees.en.txt

05_when-to-use-decision-trees.mp4

/.../04_end-of-access-to-lab-notebooks/

01_important-reminder-about-end-of-access-to-lab-notebooks_instructions.html

/.../05_conversations-with-andrew-optional/

01_andrew-ng-and-chris-manning-on-natural-language-processing.en.srt

01_andrew-ng-and-chris-manning-on-natural-language-processing.en.txt

01_andrew-ng-and-chris-manning-on-natural-language-processing.mp4

/.../04_decision-trees/06_acknowledgments/

01_acknowledgements_instructions.html

/.../01_overview-of-machine-learning/

01_welcome-to-machine-learning.en.srt

01_welcome-to-machine-learning.en.txt

01_welcome-to-machine-learning.mp4

02_applications-of-machine-learning.en.srt

02_applications-of-machine-learning.en.txt

02_applications-of-machine-learning.mp4

03_important-have-questions-issues-or-ideas-join-our-forum_instructions.html

/.../02_supervised-vs-unsupervised-machine-learning/

01_what-is-machine-learning.en.srt

01_what-is-machine-learning.en.txt

01_what-is-machine-learning.mp4

02_supervised-learning-part-1.en.srt

02_supervised-learning-part-1.en.txt

02_supervised-learning-part-1.mp4

03_supervised-learning-part-2.en.srt

03_supervised-learning-part-2.en.txt

03_supervised-learning-part-2.mp4

04_unsupervised-learning-part-1.en.srt

04_unsupervised-learning-part-1.en.txt

04_unsupervised-learning-part-1.mp4

05_unsupervised-learning-part-2.en.srt

05_unsupervised-learning-part-2.en.txt

05_unsupervised-learning-part-2.mp4

06_jupyter-notebooks.en.srt

06_jupyter-notebooks.en.txt

06_jupyter-notebooks.mp4

/.../03_regression-model/

01_linear-regression-model-part-1.en.srt

01_linear-regression-model-part-1.en.txt

01_linear-regression-model-part-1.mp4

02_linear-regression-model-part-2.en.srt

02_linear-regression-model-part-2.en.txt

02_linear-regression-model-part-2.mp4

03_cost-function-formula.en.srt

03_cost-function-formula.en.txt

03_cost-function-formula.mp4

04_cost-function-intuition.en.srt

04_cost-function-intuition.en.txt

04_cost-function-intuition.mp4

05_visualizing-the-cost-function.en.srt

05_visualizing-the-cost-function.en.txt

05_visualizing-the-cost-function.mp4

06_visualization-examples.en.srt

06_visualization-examples.en.txt

06_visualization-examples.mp4

/.../04_train-the-model-with-gradient-descent/

01_gradient-descent.en.srt

01_gradient-descent.en.txt

01_gradient-descent.mp4

02_implementing-gradient-descent.en.srt

02_implementing-gradient-descent.en.txt

02_implementing-gradient-descent.mp4

03_gradient-descent-intuition.en.srt

03_gradient-descent-intuition.en.txt

03_gradient-descent-intuition.mp4

04_learning-rate.en.srt

04_learning-rate.en.txt

04_learning-rate.mp4

05_gradient-descent-for-linear-regression.en.srt

05_gradient-descent-for-linear-regression.en.txt

05_gradient-descent-for-linear-regression.mp4

06_running-gradient-descent.en.srt

06_running-gradient-descent.en.txt

06_running-gradient-descent.mp4

/.../01_multiple-linear-regression/

01_multiple-features.en.srt

01_multiple-features.en.txt

01_multiple-features.mp4

02_vectorization-part-1.en.srt

02_vectorization-part-1.en.txt

02_vectorization-part-1.mp4

03_vectorization-part-2.en.srt

03_vectorization-part-2.en.txt

03_vectorization-part-2.mp4

04_gradient-descent-for-multiple-linear-regression.en.srt

04_gradient-descent-for-multiple-linear-regression.en.txt

04_gradient-descent-for-multiple-linear-regression.mp4

/.../02_gradient-descent-in-practice/

01_feature-scaling-part-1.en.srt

01_feature-scaling-part-1.en.txt

01_feature-scaling-part-1.mp4

02_feature-scaling-part-2.en.srt

02_feature-scaling-part-2.en.txt

02_feature-scaling-part-2.mp4

03_checking-gradient-descent-for-convergence.en.srt

03_checking-gradient-descent-for-convergence.en.txt

03_checking-gradient-descent-for-convergence.mp4

04_choosing-the-learning-rate.en.srt

04_choosing-the-learning-rate.en.txt

04_choosing-the-learning-rate.mp4

05_feature-engineering.en.srt

05_feature-engineering.en.txt

05_feature-engineering.mp4

06_polynomial-regression.en.srt

06_polynomial-regression.en.txt

06_polynomial-regression.mp4

/.../01_classification-with-logistic-regression/

01_motivations.en.srt

01_motivations.en.txt

01_motivations.mp4

02_logistic-regression.en.srt

02_logistic-regression.en.txt

02_logistic-regression.mp4

03_decision-boundary.en.srt

03_decision-boundary.en.txt

03_decision-boundary.mp4

/.../02_cost-function-for-logistic-regression/

01_cost-function-for-logistic-regression.en.srt

01_cost-function-for-logistic-regression.en.txt

01_cost-function-for-logistic-regression.mp4

02_simplified-cost-function-for-logistic-regression.en.srt

02_simplified-cost-function-for-logistic-regression.en.txt

02_simplified-cost-function-for-logistic-regression.mp4

/.../03_gradient-descent-for-logistic-regression/

01_gradient-descent-implementation.en.srt

01_gradient-descent-implementation.en.txt

01_gradient-descent-implementation.mp4

/.../04_the-problem-of-overfitting/

01_the-problem-of-overfitting.en.srt

01_the-problem-of-overfitting.en.txt

01_the-problem-of-overfitting.mp4

02_addressing-overfitting.en.srt

02_addressing-overfitting.en.txt

02_addressing-overfitting.mp4

03_cost-function-with-regularization.en.srt

03_cost-function-with-regularization.en.txt

03_cost-function-with-regularization.mp4

04_regularized-linear-regression.en.srt

04_regularized-linear-regression.en.txt

04_regularized-linear-regression.mp4

05_regularized-logistic-regression.en.srt

05_regularized-logistic-regression.en.txt

05_regularized-logistic-regression.mp4

/.../05_end-of-access-to-lab-notebooks/

01_important-reminder-about-end-of-access-to-lab-notebooks_instructions.html

/.../06_conversations-with-andrew-optional/

01_andrew-ng-and-fei-fei-li-on-human-centered-ai.en.srt

01_andrew-ng-and-fei-fei-li-on-human-centered-ai.en.txt

01_andrew-ng-and-fei-fei-li-on-human-centered-ai.mp4

/.../03_week-3-classification/07_acknowledgments/

01_acknowledgments_instructions.html

/.../01_welcome-to-the-course/

01_welcome.en.srt

01_welcome.en.txt

01_welcome.mp4

02_important-have-questions-issues-or-ideas-join-our-forum_instructions.html

/.../01_unsupervised-learning/02_clustering/

01_what-is-clustering.en.srt

01_what-is-clustering.en.txt

01_what-is-clustering.mp4

02_k-means-intuition.en.srt

02_k-means-intuition.en.txt

02_k-means-intuition.mp4

03_k-means-algorithm.en.srt

03_k-means-algorithm.en.txt

03_k-means-algorithm.mp4

04_optimization-objective.en.srt

04_optimization-objective.en.txt

04_optimization-objective.mp4

05_initializing-k-means.en.srt

05_initializing-k-means.en.txt

05_initializing-k-means.mp4

06_choosing-the-number-of-clusters.en.srt

06_choosing-the-number-of-clusters.en.txt

06_choosing-the-number-of-clusters.mp4

/.../03_anomaly-detection/

01_finding-unusual-events.en.srt

01_finding-unusual-events.en.txt

01_finding-unusual-events.mp4

02_gaussian-normal-distribution.en.srt

02_gaussian-normal-distribution.en.txt

02_gaussian-normal-distribution.mp4

03_anomaly-detection-algorithm.en.srt

03_anomaly-detection-algorithm.en.txt

03_anomaly-detection-algorithm.mp4

04_developing-and-evaluating-an-anomaly-detection-system.en.srt

04_developing-and-evaluating-an-anomaly-detection-system.en.txt

04_developing-and-evaluating-an-anomaly-detection-system.mp4

05_anomaly-detection-vs-supervised-learning.en.srt

05_anomaly-detection-vs-supervised-learning.en.txt

05_anomaly-detection-vs-supervised-learning.mp4

06_choosing-what-features-to-use.en.srt

06_choosing-what-features-to-use.en.txt

06_choosing-what-features-to-use.mp4

/.../01_collaborative-filtering/

01_making-recommendations.en.srt

01_making-recommendations.en.txt

01_making-recommendations.mp4

02_using-per-item-features.en.srt

02_using-per-item-features.en.txt

02_using-per-item-features.mp4

03_collaborative-filtering-algorithm.en.srt

03_collaborative-filtering-algorithm.en.txt

03_collaborative-filtering-algorithm.mp4

04_binary-labels-favs-likes-and-clicks.en.srt

04_binary-labels-favs-likes-and-clicks.en.txt

04_binary-labels-favs-likes-and-clicks.mp4

/.../02_recommender-systems-implementation-detail/

01_mean-normalization.en.srt

01_mean-normalization.en.txt

01_mean-normalization.mp4

02_tensorflow-implementation-of-collaborative-filtering.en.srt

02_tensorflow-implementation-of-collaborative-filtering.en.txt

02_tensorflow-implementation-of-collaborative-filtering.mp4

03_finding-related-items.en.srt

03_finding-related-items.en.txt

03_finding-related-items.mp4

/.../03_content-based-filtering/

01_collaborative-filtering-vs-content-based-filtering.en.srt

01_collaborative-filtering-vs-content-based-filtering.en.txt

01_collaborative-filtering-vs-content-based-filtering.mp4

02_deep-learning-for-content-based-filtering.en.srt

02_deep-learning-for-content-based-filtering.en.txt

02_deep-learning-for-content-based-filtering.mp4

03_recommending-from-a-large-catalogue.en.srt

03_recommending-from-a-large-catalogue.en.txt

03_recommending-from-a-large-catalogue.mp4

04_ethical-use-of-recommender-systems.en.srt

04_ethical-use-of-recommender-systems.en.txt

04_ethical-use-of-recommender-systems.mp4

05_tensorflow-implementation-of-content-based-filtering.en.srt

05_tensorflow-implementation-of-content-based-filtering.en.txt

05_tensorflow-implementation-of-content-based-filtering.mp4

/.../04_principal-component-analysis/

01_reducing-the-number-of-features-optional.en.srt

01_reducing-the-number-of-features-optional.en.txt

01_reducing-the-number-of-features-optional.mp4

02_pca-algorithm-optional.en.srt

02_pca-algorithm-optional.en.txt

02_pca-algorithm-optional.mp4

03_pca-in-code-optional.en.srt

03_pca-in-code-optional.en.txt

03_pca-in-code-optional.mp4

/.../01_reinforcement-learning-introduction/

01_what-is-reinforcement-learning.en.srt

01_what-is-reinforcement-learning.en.txt

01_what-is-reinforcement-learning.mp4

02_mars-rover-example.en.srt

02_mars-rover-example.en.txt

02_mars-rover-example.mp4

03_the-return-in-reinforcement-learning.en.srt

03_the-return-in-reinforcement-learning.en.txt

03_the-return-in-reinforcement-learning.mp4

04_making-decisions-policies-in-reinforcement-learning.en.srt

04_making-decisions-policies-in-reinforcement-learning.en.txt

04_making-decisions-policies-in-reinforcement-learning.mp4

05_review-of-key-concepts.en.srt

05_review-of-key-concepts.en.txt

05_review-of-key-concepts.mp4

/.../02_state-action-value-function/

01_state-action-value-function-definition.en.srt

01_state-action-value-function-definition.en.txt

01_state-action-value-function-definition.mp4

02_state-action-value-function-example.en.srt

02_state-action-value-function-example.en.txt

02_state-action-value-function-example.mp4

03_bellman-equation.en.srt

03_bellman-equation.en.txt

03_bellman-equation.mp4

04_random-stochastic-environment-optional.en.srt

04_random-stochastic-environment-optional.en.txt

04_random-stochastic-environment-optional.mp4

/.../03_continuous-state-spaces/

01_example-of-continuous-state-space-applications.en.srt

01_example-of-continuous-state-space-applications.en.txt

01_example-of-continuous-state-space-applications.mp4

02_lunar-lander.en.srt

02_lunar-lander.en.txt

02_lunar-lander.mp4

03_learning-the-state-value-function.en.srt

03_learning-the-state-value-function.en.txt

03_learning-the-state-value-function.mp4

04_algorithm-refinement-improved-neural-network-architecture.en.srt

04_algorithm-refinement-improved-neural-network-architecture.en.txt

04_algorithm-refinement-improved-neural-network-architecture.mp4

05_algorithm-refinement-greedy-policy.en.srt

05_algorithm-refinement-greedy-policy.en.txt

05_algorithm-refinement-greedy-policy.mp4

06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.srt

06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.txt

06_algorithm-refinement-mini-batch-and-soft-updates-optional.mp4

07_the-state-of-reinforcement-learning.en.srt

07_the-state-of-reinforcement-learning.en.txt

07_the-state-of-reinforcement-learning.mp4

/.../04_end-of-access-to-lab-notebooks/

01_important-reminder-about-end-of-access-to-lab-notebooks_instructions.html

/.../05_summary-and-thank-you/

01_summary-and-thank-you.en.srt

01_summary-and-thank-you.en.txt

01_summary-and-thank-you.mp4

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01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.srt

01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.txt

01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.mp4

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01_acknowledgments_instructions.html

02_optional-opportunity-to-mentor-other-learners_instructions.html

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