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  advanced-learning-algorithms

   01_neural-networks

    01_neural-networks-intuition

     01_welcome.en.txt

     02_neurons-and-the-brain.en.srt

     02_neurons-and-the-brain.mp4

     03_demand-prediction.en.txt

     04_example-recognizing-images.en.srt

     04_example-recognizing-images.mp4

    02_neural-network-model

     01_neural-network-layer.en.txt

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

     02_more-complex-neural-networks.mp4

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

    03_tensorflow-implementation

     01_inference-in-code.en.txt

     02_data-in-tensorflow.en.srt

     02_data-in-tensorflow.mp4

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

    04_neural-network-implementation-in-python

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

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

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

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

     01_is-there-a-path-to-agi.en.txt

    06_vectorization-optional

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

     02_matrix-multiplication.en.srt

     02_matrix-multiplication.mp4

     03_matrix-multiplication-rules.en.txt

     04_matrix-multiplication-code.en.srt

     04_matrix-multiplication-code.mp4

   02_neural-network-training

    01_neural-network-training

     01_tensorflow-implementation.en.txt

     02_training-details.en.srt

     02_training-details.mp4

    02_activation-functions

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

     02_choosing-activation-functions.en.srt

     02_choosing-activation-functions.mp4

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

    03_multiclass-classification

     01_multiclass.en.txt

     02_softmax.en.srt

     02_softmax.mp4

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

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

     04_improved-implementation-of-softmax.mp4

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

    04_additional-neural-network-concepts

     01_advanced-optimization.en.txt

     02_additional-layer-types.en.srt

     02_additional-layer-types.mp4

    05_back-propagation-optional

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

     02_computation-graph-optional.en.srt

     02_computation-graph-optional.mp4

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

   03_advice-for-applying-machine-learning

    01_advice-for-applying-machine-learning

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

     02_evaluating-a-model.en.srt

     02_evaluating-a-model.mp4

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

    02_bias-and-variance

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

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

     02_regularization-and-bias-variance.mp4

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

     04_learning-curves.en.srt

     04_learning-curves.mp4

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

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

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

    03_machine-learning-development-process

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

     02_error-analysis.en.srt

     02_error-analysis.mp4

     03_adding-data.en.txt

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

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

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

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

     06_fairness-bias-and-ethics.mp4

    04_skewed-datasets-optional

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

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

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

   04_decision-trees

    01_decision-trees

     01_decision-tree-model.en.txt

     02_learning-process.en.srt

     02_learning-process.mp4

    02_decision-tree-learning

     01_measuring-purity.en.txt

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

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

     03_putting-it-together.en.txt

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

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

     05_continuous-valued-features.en.txt

     06_regression-trees-optional.en.srt

     06_regression-trees-optional.mp4

    03_tree-ensembles

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

     02_sampling-with-replacement.en.srt

     02_sampling-with-replacement.mp4

     03_random-forest-algorithm.en.txt

     04_xgboost.en.srt

     04_xgboost.mp4

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

    04_conversations-with-andrew-optional

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

    05_acknowledgments

  machine-learning

   01_week-1-introduction-to-machine-learning

    01_overview-of-machine-learning

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

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

     02_applications-of-machine-learning.mp4

    02_supervised-vs-unsupervised-machine-learning

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

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

     02_supervised-learning-part-1.mp4

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

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

     04_unsupervised-learning-part-1.mp4

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

     06_jupyter-notebooks.en.srt

     06_jupyter-notebooks.mp4

    03_practice-quiz-supervised-vs-unsupervised-learning

    04_regression-model

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

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

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

     03_cost-function-formula.en.txt

     04_cost-function-intuition.en.srt

     04_cost-function-intuition.mp4

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

     06_visualization-examples.en.srt

     06_visualization-examples.mp4

    05_practice-quiz-regression-model

    06_train-the-model-with-gradient-descent

     01_gradient-descent.en.txt

     02_implementing-gradient-descent.en.srt

     02_implementing-gradient-descent.mp4

     03_gradient-descent-intuition.en.txt

     04_learning-rate.en.srt

     04_learning-rate.mp4

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

     06_running-gradient-descent.en.srt

     06_running-gradient-descent.mp4

    07_practice-quiz-train-the-model-with-gradient-descent

   02_week-2-regression-with-multiple-input-variables

    01_multiple-linear-regression

     01_multiple-features.en.txt

     02_vectorization-part-1.en.srt

     02_vectorization-part-1.mp4

     03_vectorization-part-2.en.txt

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

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

    02_practice-quiz-multiple-linear-regression

    03_gradient-descent-in-practice

     01_feature-scaling-part-1.en.txt

     02_feature-scaling-part-2.en.srt

     02_feature-scaling-part-2.mp4

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

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

     04_choosing-the-learning-rate.mp4

     05_feature-engineering.en.txt

     06_polynomial-regression.en.srt

     06_polynomial-regression.mp4

    04_practice-quiz-gradient-descent-in-practice

    05_week-2-practice-lab-linear-regression

   03_week-3-classification

    01_classification-with-logistic-regression

     01_motivations.en.txt

     02_logistic-regression.en.srt

     02_logistic-regression.mp4

     03_decision-boundary.en.txt

    02_practice-quiz-classification-with-logistic-regression

    03_cost-function-for-logistic-regression

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

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

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

    04_practice-quiz-cost-function-for-logistic-regression

    05_gradient-descent-for-logistic-regression

     01_gradient-descent-implementation.en.txt

    06_practice-quiz-gradient-descent-for-logistic-regression

    07_the-problem-of-overfitting

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

     02_addressing-overfitting.en.srt

     02_addressing-overfitting.mp4

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

     04_regularized-linear-regression.en.srt

     04_regularized-linear-regression.mp4

     05_regularized-logistic-regression.en.txt

    08_practice-quiz-the-problem-of-overfitting

    09_week-3-practice-lab-logistic-regression

    10_conversations-with-andrew-optional

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

    11_acknowledgments

  unsupervised-learning-recommenders-reinforcement-learning

   01_unsupervised-learning

    01_welcome-to-the-course

     01_welcome.en.txt

    02_clustering

     01_what-is-clustering.en.txt

     02_k-means-intuition.en.srt

     02_k-means-intuition.mp4

     03_k-means-algorithm.en.txt

     04_optimization-objective.en.srt

     04_optimization-objective.mp4

     05_initializing-k-means.en.txt

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

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

    03_anomaly-detection

     01_finding-unusual-events.en.txt

     02_gaussian-normal-distribution.en.srt

     02_gaussian-normal-distribution.mp4

     03_anomaly-detection-algorithm.en.txt

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

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

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

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

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

   02_recommender-systems

    01_collaborative-filtering

     01_making-recommendations.en.txt

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

     02_using-per-item-features.mp4

     03_collaborative-filtering-algorithm.en.txt

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

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

    02_recommender-systems-implementation-detail

     01_mean-normalization.en.txt

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

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

     03_finding-related-items.en.txt

    03_content-based-filtering

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

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

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

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

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

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

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

    04_principal-component-analysis

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

     02_pca-algorithm-optional.en.srt

     02_pca-algorithm-optional.mp4

     03_pca-in-code-optional.en.txt

   03_reinforcement-learning

    01_reinforcement-learning-introduction

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

     02_mars-rover-example.en.srt

     02_mars-rover-example.mp4

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

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

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

     05_review-of-key-concepts.en.txt

    02_state-action-value-function

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

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

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

     03_bellman-equation.en.txt

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

     04_random-stochastic-environment-optional.mp4

    03_continuous-state-spaces

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

     02_lunar-lander.en.srt

     02_lunar-lander.mp4

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

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

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

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

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

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

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

    04_summary-and-thank-you

     01_summary-and-thank-you.en.txt

    05_conversations-with-andrew-optional

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

    06_acknowledgments

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