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Machine Learning Specialization
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[TutsNode.net] - Machine Learning Specialization
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advanced-learning-algorithms
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04_decision-trees
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04_conversations-with-andrew-optional
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01_andrew-ng-and-chris-manning-on-natural-language-processing.mp4 - 236.15 MB
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01_andrew-ng-and-chris-manning-on-natural-language-processing.en.srt - 63.95 KB
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01_andrew-ng-and-chris-manning-on-natural-language-processing.en.txt - 39.86 KB
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01_decision-trees
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02_learning-process.mp4 - 29.03 MB
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01_decision-tree-model.mp4 - 14.76 MB
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02_learning-process.en.srt - 18.03 KB
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01_decision-tree-model.en.srt - 10.84 KB
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02_learning-process.en.txt - 9.42 KB
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01_decision-tree-model.en.txt - 5.65 KB
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02_decision-tree-learning
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02_choosing-a-split-information-gain.mp4 - 23.77 MB
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06_regression-trees-optional.mp4 - 18.9 MB
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03_putting-it-together.mp4 - 18.41 MB
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01_measuring-purity.mp4 - 15.97 MB
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05_continuous-valued-features.mp4 - 15.89 MB
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04_using-one-hot-encoding-of-categorical-features.mp4 - 14.17 MB
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02_choosing-a-split-information-gain.en.srt - 17.43 KB
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03_putting-it-together.en.srt - 14.59 KB
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06_regression-trees-optional.en.srt - 12.17 KB
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01_measuring-purity.en.srt - 10.17 KB
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02_choosing-a-split-information-gain.en.txt - 8.96 KB
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05_continuous-valued-features.en.srt - 8.61 KB
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03_putting-it-together.en.txt - 7.76 KB
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06_regression-trees-optional.en.txt - 7.73 KB
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04_using-one-hot-encoding-of-categorical-features.en.srt - 6.6 KB
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05_continuous-valued-features.en.txt - 5.5 KB
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01_measuring-purity.en.txt - 5.39 KB
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04_using-one-hot-encoding-of-categorical-features.en.txt - 4.32 KB
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03_tree-ensembles
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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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02_sampling-with-replacement.mp4 - 14.33 MB
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03_random-forest-algorithm.mp4 - 12.72 MB
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01_using-multiple-decision-trees.mp4 - 12.51 MB
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05_when-to-use-decision-trees.en.srt - 10.46 KB
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04_xgboost.en.srt - 9.42 KB
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03_random-forest-algorithm.en.srt - 8.75 KB
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02_sampling-with-replacement.en.srt - 6.52 KB
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01_using-multiple-decision-trees.en.srt - 6.44 KB
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04_xgboost.en.txt - 6.04 KB
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05_when-to-use-decision-trees.en.txt - 5.58 KB
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03_random-forest-algorithm.en.txt - 5.57 KB
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02_sampling-with-replacement.en.txt - 3.41 KB
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01_using-multiple-decision-trees.en.txt - 3.39 KB
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05_acknowledgments
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01_acknowledgements_instructions.html - 5.3 KB
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02_neural-network-training
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05_back-propagation-optional
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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_what-is-a-derivative-optional.en.srt - 29.72 KB
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02_computation-graph-optional.en.srt - 26.39 KB
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01_what-is-a-derivative-optional.en.txt - 15.2 KB
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02_computation-graph-optional.en.txt - 13.61 KB
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03_larger-neural-network-example-optional.en.srt - 11.92 KB
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03_larger-neural-network-example-optional.en.txt - 7.31 KB
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01_neural-network-training
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02_training-details.mp4 - 24.08 MB
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01_tensorflow-implementation.mp4 - 11.4 MB
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02_training-details.en.srt - 20.89 KB
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02_training-details.en.txt - 10.91 KB
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01_tensorflow-implementation.en.srt - 5.94 KB
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01_tensorflow-implementation.en.txt - 3.09 KB
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02_activation-functions
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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_alternatives-to-the-sigmoid-activation.mp4 - 11.96 MB
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02_choosing-activation-functions.en.srt - 13.76 KB
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03_why-do-we-need-activation-functions.en.srt - 7.63 KB
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02_choosing-activation-functions.en.txt - 7.24 KB
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01_alternatives-to-the-sigmoid-activation.en.srt - 6.83 KB
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01_alternatives-to-the-sigmoid-activation.en.txt - 4.31 KB
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03_why-do-we-need-activation-functions.en.txt - 4.01 KB
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03_multiclass-classification
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02_softmax.mp4 - 20.69 MB
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04_improved-implementation-of-softmax.mp4 - 15.05 MB
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03_neural-network-with-softmax-output.mp4 - 15.03 MB
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05_classification-with-multiple-outputs-optional.mp4 - 11.31 MB
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01_multiclass.mp4 - 8.37 MB
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02_softmax.en.srt - 15.38 KB
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04_improved-implementation-of-softmax.en.srt - 13.39 KB
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03_neural-network-with-softmax-output.en.srt - 9.07 KB
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02_softmax.en.txt - 7.93 KB
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04_improved-implementation-of-softmax.en.txt - 7.07 KB
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05_classification-with-multiple-outputs-optional.en.srt - 6.76 KB
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03_neural-network-with-softmax-output.en.txt - 5.76 KB
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01_multiclass.en.srt - 4.28 KB
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05_classification-with-multiple-outputs-optional.en.txt - 3.58 KB
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01_multiclass.en.txt - 2.76 KB
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04_additional-neural-network-concepts
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02_additional-layer-types.mp4 - 19.53 MB
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01_advanced-optimization.mp4 - 15.57 MB
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02_additional-layer-types.en.srt - 11.31 KB
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01_advanced-optimization.en.srt - 10.54 KB
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02_additional-layer-types.en.txt - 7.24 KB
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01_advanced-optimization.en.txt - 5.56 KB
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03_advice-for-applying-machine-learning
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03_machine-learning-development-process
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03_adding-data.mp4 - 32.94 MB
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06_fairness-bias-and-ethics.mp4 - 25.35 MB
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04_transfer-learning-using-data-from-a-different-task.mp4 - 19.02 MB
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02_error-analysis.mp4 - 17.51 MB
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05_full-cycle-of-a-machine-learning-project.mp4 - 16.35 MB
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01_iterative-loop-of-ml-development.mp4 - 14.81 MB
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04_transfer-learning-using-data-from-a-different-task.en.srt - 20.17 KB
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03_adding-data.en.srt - 18.94 KB
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05_full-cycle-of-a-machine-learning-project.en.srt - 14.53 KB
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06_fairness-bias-and-ethics.en.srt - 13.43 KB
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02_error-analysis.en.srt - 13.23 KB
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01_iterative-loop-of-ml-development.en.srt - 12.14 KB
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03_adding-data.en.txt - 12.04 KB
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04_transfer-learning-using-data-from-a-different-task.en.txt - 10.54 KB
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06_fairness-bias-and-ethics.en.txt - 8.64 KB
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05_full-cycle-of-a-machine-learning-project.en.txt - 7.7 KB
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02_error-analysis.en.txt - 7.05 KB
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01_iterative-loop-of-ml-development.en.txt - 6.44 KB
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01_advice-for-applying-machine-learning
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03_model-selection-and-training-cross-validation-test-sets.mp4 - 29.62 MB
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02_evaluating-a-model.mp4 - 19.45 MB
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01_deciding-what-to-try-next.mp4 - 11.45 MB
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03_model-selection-and-training-cross-validation-test-sets.en.srt - 21.28 KB
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02_evaluating-a-model.en.srt - 12.81 KB
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03_model-selection-and-training-cross-validation-test-sets.en.txt - 11.23 KB
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02_evaluating-a-model.en.txt - 8.14 KB
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01_deciding-what-to-try-next.en.srt - 6.77 KB
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01_deciding-what-to-try-next.en.txt - 3.56 KB
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02_bias-and-variance
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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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04_learning-curves.mp4 - 23.28 MB
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02_regularization-and-bias-variance.mp4 - 21.11 MB
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01_diagnosing-bias-and-variance.mp4 - 20.3 MB
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03_establishing-a-baseline-level-of-performance.mp4 - 19.39 MB
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04_learning-curves.en.srt - 20.05 KB
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01_diagnosing-bias-and-variance.en.srt - 17.79 KB
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02_regularization-and-bias-variance.en.srt - 16.26 KB
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03_establishing-a-baseline-level-of-performance.en.srt - 15.85 KB
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06_bias-variance-and-neural-networks.en.srt - 14.58 KB
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05_deciding-what-to-try-next-revisited.en.srt - 14.51 KB
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04_learning-curves.en.txt - 10.49 KB
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06_bias-variance-and-neural-networks.en.txt - 9.46 KB
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01_diagnosing-bias-and-variance.en.txt - 9.25 KB
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02_regularization-and-bias-variance.en.txt - 8.3 KB
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03_establishing-a-baseline-level-of-performance.en.txt - 8.26 KB
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05_deciding-what-to-try-next-revisited.en.txt - 7.75 KB
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04_skewed-datasets-optional
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02_trading-off-precision-and-recall.mp4 - 22.17 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.en.srt - 18.29 KB
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01_error-metrics-for-skewed-datasets.en.srt - 17.02 KB
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02_trading-off-precision-and-recall.en.txt - 9.58 KB
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01_error-metrics-for-skewed-datasets.en.txt - 8.89 KB
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01_neural-networks
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05_speculations-on-artificial-general-intelligence-agi
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01_is-there-a-path-to-agi.mp4 - 28.09 MB
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01_is-there-a-path-to-agi.en.srt - 16.42 KB
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01_is-there-a-path-to-agi.en.txt - 8.59 KB
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01_neural-networks-intuition
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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_welcome.mp4 - 10.64 MB
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03_demand-prediction.en.srt - 26.6 KB
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02_neurons-and-the-brain.en.srt - 18.27 KB
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03_demand-prediction.en.txt - 13.99 KB
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04_example-recognizing-images.en.srt - 10.18 KB
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02_neurons-and-the-brain.en.txt - 9.52 KB
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01_welcome.en.srt - 5.36 KB
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04_example-recognizing-images.en.txt - 5.33 KB
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01_welcome.en.txt - 2.84 KB
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03_tensorflow-implementation
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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_inference-in-code.mp4 - 16.82 MB
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02_data-in-tensorflow.en.srt - 13.17 KB
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03_building-a-neural-network.en.srt - 10.69 KB
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01_inference-in-code.en.srt - 10.03 KB
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02_data-in-tensorflow.en.txt - 8.25 KB
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03_building-a-neural-network.en.txt - 6.87 KB
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01_inference-in-code.en.txt - 5.25 KB
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04_neural-network-implementation-in-python
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02_general-implementation-of-forward-propagation.mp4 - 21.33 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.en.srt - 11.61 KB
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01_forward-prop-in-a-single-layer.en.srt - 6.05 KB
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02_general-implementation-of-forward-propagation.en.txt - 6.01 KB
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01_forward-prop-in-a-single-layer.en.txt - 3.84 KB
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02_neural-network-model
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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_neural-network-layer.en.srt - 12.99 KB
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02_more-complex-neural-networks.en.srt - 11.73 KB
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03_inference-making-predictions-forward-propagation.en.srt - 6.96 KB
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01_neural-network-layer.en.txt - 6.84 KB
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02_more-complex-neural-networks.en.txt - 6.06 KB
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03_inference-making-predictions-forward-propagation.en.txt - 4.34 KB
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06_vectorization-optional
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03_matrix-multiplication-rules.mp4 - 16.14 MB
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02_matrix-multiplication.mp4 - 15.89 MB
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04_matrix-multiplication-code.mp4 - 13.38 MB
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01_how-neural-networks-are-implemented-efficiently.mp4 - 12.24 MB
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02_matrix-multiplication.en.srt - 12.33 KB
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03_matrix-multiplication-rules.en.srt - 11.38 KB
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04_matrix-multiplication-code.en.srt - 8.82 KB
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03_matrix-multiplication-rules.en.txt - 7.08 KB
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02_matrix-multiplication.en.txt - 6.39 KB
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01_how-neural-networks-are-implemented-efficiently.en.srt - 6.12 KB
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04_matrix-multiplication-code.en.txt - 4.58 KB
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01_how-neural-networks-are-implemented-efficiently.en.txt - 3.17 KB
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unsupervised-learning-recommenders-reinforcement-learning
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03_reinforcement-learning
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05_conversations-with-andrew-optional
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01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.mp4 - 230.66 MB
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01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.srt - 50.57 KB
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01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.txt - 31.74 KB
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03_continuous-state-spaces
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03_learning-the-state-value-function.mp4 - 31.14 MB
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01_example-of-continuous-state-space-applications.mp4 - 27.05 MB
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06_algorithm-refinement-mini-batch-and-soft-updates-optional.mp4 - 25.55 MB
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05_algorithm-refinement-greedy-policy.mp4 - 25.27 MB
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02_lunar-lander.mp4 - 10.37 MB
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07_the-state-of-reinforcement-learning.mp4 - 7.86 MB
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04_algorithm-refinement-improved-neural-network-architecture.mp4 - 7.79 MB
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03_learning-the-state-value-function.en.srt - 25.16 KB
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06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.srt - 17.71 KB
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05_algorithm-refinement-greedy-policy.en.srt - 14.24 KB
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03_learning-the-state-value-function.en.txt - 12.93 KB
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01_example-of-continuous-state-space-applications.en.srt - 9.68 KB
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06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.txt - 9.44 KB
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02_lunar-lander.en.srt - 7.76 KB
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05_algorithm-refinement-greedy-policy.en.txt - 7.54 KB
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01_example-of-continuous-state-space-applications.en.txt - 5.03 KB
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02_lunar-lander.en.txt - 4.92 KB
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04_algorithm-refinement-improved-neural-network-architecture.en.srt - 4.71 KB
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07_the-state-of-reinforcement-learning.en.srt - 4.04 KB
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07_the-state-of-reinforcement-learning.en.txt - 2.63 KB
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04_algorithm-refinement-improved-neural-network-architecture.en.txt - 2.46 KB
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01_reinforcement-learning-introduction
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01_what-is-reinforcement-learning.mp4 - 30.97 MB
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03_the-return-in-reinforcement-learning.mp4 - 29.01 MB
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02_mars-rover-example.mp4 - 12.65 MB
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05_review-of-key-concepts.mp4 - 11.39 MB
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04_making-decisions-policies-in-reinforcement-learning.mp4 - 5.81 MB
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03_the-return-in-reinforcement-learning.en.srt - 15.66 KB
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01_what-is-reinforcement-learning.en.srt - 12.51 KB
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02_mars-rover-example.en.srt - 10.32 KB
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05_review-of-key-concepts.en.srt - 8.48 KB
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03_the-return-in-reinforcement-learning.en.txt - 8.18 KB
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01_what-is-reinforcement-learning.en.txt - 8 KB
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02_mars-rover-example.en.txt - 5.33 KB
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05_review-of-key-concepts.en.txt - 4.51 KB
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04_making-decisions-policies-in-reinforcement-learning.en.srt - 3.74 KB
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04_making-decisions-policies-in-reinforcement-learning.en.txt - 1.99 KB
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02_state-action-value-function
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03_bellman-equation.mp4 - 26.66 MB
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01_state-action-value-function-definition.mp4 - 19.84 MB
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04_random-stochastic-environment-optional.mp4 - 19.27 MB
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02_state-action-value-function-example.mp4 - 14.64 MB
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03_bellman-equation.en.srt - 17.73 KB
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01_state-action-value-function-definition.en.srt - 13.52 KB
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04_random-stochastic-environment-optional.en.srt - 13.1 KB
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03_bellman-equation.en.txt - 9.22 KB
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01_state-action-value-function-definition.en.txt - 8.3 KB
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04_random-stochastic-environment-optional.en.txt - 6.92 KB
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02_state-action-value-function-example.en.srt - 6.77 KB
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02_state-action-value-function-example.en.txt - 4.25 KB
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04_summary-and-thank-you
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01_summary-and-thank-you.mp4 - 13.94 MB
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01_summary-and-thank-you.en.srt - 5.27 KB
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01_summary-and-thank-you.en.txt - 2.79 KB
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06_acknowledgments
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01_acknowledgments_instructions.html - 5.3 KB
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02_optional-opportunity-to-mentor-other-learners_instructions.html - 1.66 KB
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02_recommender-systems
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02_recommender-systems-implementation-detail
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02_tensorflow-implementation-of-collaborative-filtering.mp4 - 35.87 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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02_tensorflow-implementation-of-collaborative-filtering.en.srt - 15.45 KB
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01_mean-normalization.en.srt - 10.72 KB
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03_finding-related-items.en.srt - 10.21 KB
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02_tensorflow-implementation-of-collaborative-filtering.en.txt - 9.57 KB
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01_mean-normalization.en.txt - 6.73 KB
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03_finding-related-items.en.txt - 5.4 KB
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01_collaborative-filtering
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03_collaborative-filtering-algorithm.mp4 - 31.03 MB
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02_using-per-item-features.mp4 - 23.49 MB
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01_making-recommendations.mp4 - 20.44 MB
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04_binary-labels-favs-likes-and-clicks.mp4 - 19.84 MB
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03_collaborative-filtering-algorithm.en.srt - 18.78 KB
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02_using-per-item-features.en.srt - 12.65 KB
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04_binary-labels-favs-likes-and-clicks.en.srt - 10.61 KB
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03_collaborative-filtering-algorithm.en.txt - 10 KB
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02_using-per-item-features.en.txt - 7.76 KB
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01_making-recommendations.en.srt - 7.2 KB
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04_binary-labels-favs-likes-and-clicks.en.txt - 6.68 KB
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01_making-recommendations.en.txt - 4.59 KB
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04_principal-component-analysis
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02_pca-algorithm-optional.mp4 - 28.01 MB
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01_reducing-the-number-of-features-optional.mp4 - 26.7 MB
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03_pca-in-code-optional.mp4 - 17.8 MB
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02_pca-algorithm-optional.en.srt - 24.43 KB
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01_reducing-the-number-of-features-optional.en.srt - 18.06 KB
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03_pca-in-code-optional.en.srt - 16.73 KB
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02_pca-algorithm-optional.en.txt - 12.81 KB
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01_reducing-the-number-of-features-optional.en.txt - 9.38 KB
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03_pca-in-code-optional.en.txt - 8.64 KB
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03_content-based-filtering
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04_ethical-use-of-recommender-systems.mp4 - 24.83 MB
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02_deep-learning-for-content-based-filtering.mp4 - 24.34 MB
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01_collaborative-filtering-vs-content-based-filtering.mp4 - 19.97 MB
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03_recommending-from-a-large-catalogue.mp4 - 17.98 MB
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05_tensorflow-implementation-of-content-based-filtering.mp4 - 12.94 MB
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04_ethical-use-of-recommender-systems.en.srt - 16.92 KB
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01_collaborative-filtering-vs-content-based-filtering.en.srt - 14.32 KB
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02_deep-learning-for-content-based-filtering.en.srt - 14.18 KB
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03_recommending-from-a-large-catalogue.en.srt - 10.2 KB
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04_ethical-use-of-recommender-systems.en.txt - 8.96 KB
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01_collaborative-filtering-vs-content-based-filtering.en.txt - 7.42 KB
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02_deep-learning-for-content-based-filtering.en.txt - 7.42 KB
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05_tensorflow-implementation-of-content-based-filtering.en.srt - 7.29 KB
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03_recommending-from-a-large-catalogue.en.txt - 6.37 KB
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05_tensorflow-implementation-of-content-based-filtering.en.txt - 3.87 KB
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01_unsupervised-learning
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03_anomaly-detection
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06_choosing-what-features-to-use.mp4 - 30.87 MB
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01_finding-unusual-events.mp4 - 26.28 MB
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04_developing-and-evaluating-an-anomaly-detection-system.mp4 - 23.9 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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05_anomaly-detection-vs-supervised-learning.mp4 - 20.31 MB
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06_choosing-what-features-to-use.en.srt - 18.66 KB
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04_developing-and-evaluating-an-anomaly-detection-system.en.srt - 17.51 KB
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02_gaussian-normal-distribution.en.srt - 14.96 KB
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01_finding-unusual-events.en.srt - 14.54 KB
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03_anomaly-detection-algorithm.en.srt - 13.34 KB
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06_choosing-what-features-to-use.en.txt - 11.93 KB
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02_you-ve-made-it.en.srt - 1.04 KB
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02_you-ve-made-it.en.txt - 636 B
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02_regression-predicting-house-prices
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04_predicting-house-prices-jupyter-notebook
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11_applying-learned-models-to-predict-price-of-two-fancy-houses.mp4 - 22.37 MB
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02_loading-exploring-house-sale-data.mp4 - 18.41 MB
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08_exploring-other-features-of-the-data.mp4 - 13.66 MB
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10_applying-learned-models-to-predict-price-of-an-average-house.mp4 - 12.65 MB
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06_visualizing-predictions-of-simple-model-with-matplotlib.mp4 - 11.87 MB
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04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.mp4 - 8.47 MB
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09_learning-a-model-to-predict-house-prices-from-more-features.mp4 - 7.73 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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01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_house_images.zip - 4.66 MB
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07_inspecting-the-model-coefficients-learned.mp4 - 3.39 MB
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01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_home_data.sframe.zip - 908.17 KB
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01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND02-NB01.ipynb.zip - 67.26 KB
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11_applying-learned-models-to-predict-price-of-two-fancy-houses.en.srt - 8.24 KB
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02_loading-exploring-house-sale-data.en.srt - 7.88 KB
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08_exploring-other-features-of-the-data.en.srt - 7.38 KB
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10_applying-learned-models-to-predict-price-of-an-average-house.en.srt - 5.45 KB
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06_visualizing-predictions-of-simple-model-with-matplotlib.en.srt - 5.35 KB
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02_loading-exploring-house-sale-data.en.txt - 4.87 KB
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11_applying-learned-models-to-predict-price-of-two-fancy-houses.en.txt - 4.58 KB
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08_exploring-other-features-of-the-data.en.txt - 4.37 KB
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04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.en.srt - 4.32 KB
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09_learning-a-model-to-predict-house-prices-from-more-features.en.srt - 3.61 KB
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10_applying-learned-models-to-predict-price-of-an-average-house.en.txt - 3.24 KB
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06_visualizing-predictions-of-simple-model-with-matplotlib.en.txt - 3.23 KB
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03_splitting-the-data-into-training-and-test-sets.en.srt - 2.9 KB
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05_evaluating-error-rmse-of-the-simple-model.en.srt - 2.59 KB
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04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.en.txt - 2.57 KB
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01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html - 2.54 KB
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09_learning-a-model-to-predict-house-prices-from-more-features.en.txt - 2.12 KB
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03_splitting-the-data-into-training-and-test-sets.en.txt - 1.75 KB
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07_inspecting-the-model-coefficients-learned.en.srt - 1.52 KB
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05_evaluating-error-rmse-of-the-simple-model.en.txt - 1.47 KB
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07_inspecting-the-model-coefficients-learned.en.txt - 923 B
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01_linear-regression-modeling
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01_slides-presented-in-this-module_regression-intro-annotated.pdf - 21.53 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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03_what-is-the-goal-and-how-might-you-naively-address-it.mp4 - 10.06 MB
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02_predicting-house-prices-a-case-study-in-regression.mp4 - 3.83 MB
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04_linear-regression-a-model-based-approach.en.srt - 6.87 KB
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05_adding-higher-order-effects.en.srt - 6.07 KB
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03_what-is-the-goal-and-how-might-you-naively-address-it.en.srt - 4.94 KB
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04_linear-regression-a-model-based-approach.en.txt - 4.09 KB
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05_adding-higher-order-effects.en.txt - 3.55 KB
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03_what-is-the-goal-and-how-might-you-naively-address-it.en.txt - 3.09 KB
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02_predicting-house-prices-a-case-study-in-regression.en.srt - 2.03 KB
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02_predicting-house-prices-a-case-study-in-regression.en.txt - 1.24 KB
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01_slides-presented-in-this-module_instructions.html - 1.17 KB
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03_summary-of-regression
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01_regression-ml-block-diagram.mp4 - 15.47 MB
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02_regression_exam.html - 676.92 KB
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01_regression-ml-block-diagram.en.srt - 7.79 KB
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01_regression-ml-block-diagram.en.txt - 4.92 KB
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02_evaluating-regression-models
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01_evaluating-overfitting-via-training-test-split.mp4 - 15.44 MB
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04_other-regression-examples.mp4 - 13.48 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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01_evaluating-overfitting-via-training-test-split.en.srt - 8.64 KB
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02_training-test-curves.en.srt - 5.51 KB
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01_evaluating-overfitting-via-training-test-split.en.txt - 5.46 KB
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04_other-regression-examples.en.srt - 5.01 KB
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03_adding-other-features.en.srt - 3.79 KB
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02_training-test-curves.en.txt - 3.33 KB
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04_other-regression-examples.en.txt - 3.23 KB
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03_adding-other-features.en.txt - 2.31 KB
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05_programming-assignment
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01_predicting-house-prices-assignment_house_images.zip - 4.66 MB
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01_predicting-house-prices-assignment_home_data.csv - 2.48 MB
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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_graphlab.SFrame.html - 281.42 KB
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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_instructions.html - 29.36 KB
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02_predicting-house-prices_exam.html - 4.17 KB
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08_Resources
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01_discussion-forums-and-mentors
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01__MentorProgramInformation.pdf - 62.75 KB
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01__resources.html - 4.04 KB
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ml-clustering-and-retrieval
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05_mixed-membership-modeling-via-latent-dirichlet-allocation
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05_programming-assignment
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01_modeling-text-topics-with-latent-dirichlet-allocation_topic_models.zip - 87.67 MB
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01_modeling-text-topics-with-latent-dirichlet-allocation_people_wiki.sframe.zip - 56.24 MB
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01_modeling-text-topics-with-latent-dirichlet-allocation_CLU05-NB01.ipynb.zip - 8.95 KB
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01_modeling-text-topics-with-latent-dirichlet-allocation_instructions.html - 5.53 KB
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02_bayesian-inference-via-gibbs-sampling
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03_a-standard-gibbs-sampler-for-lda.mp4 - 29.27 MB
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02_gibbs-sampling-from-10-000-feet.mp4 - 18.44 MB
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01_the-need-for-bayesian-inference.mp4 - 17.01 MB
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03_a-standard-gibbs-sampler-for-lda.en.srt - 11.4 KB
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02_gibbs-sampling-from-10-000-feet.en.srt - 7.45 KB
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03_a-standard-gibbs-sampler-for-lda.en.txt - 7.17 KB
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01_the-need-for-bayesian-inference.en.srt - 6.75 KB
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02_gibbs-sampling-from-10-000-feet.en.txt - 4.8 KB
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01_the-need-for-bayesian-inference.en.txt - 4.33 KB
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01_introduction-to-latent-dirichlet-allocation
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05_goal-of-lda-inference.mp4 - 18.22 MB
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03_an-alternative-document-clustering-model.mp4 - 16.58 MB
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02_mixed-membership-models-for-documents.mp4 - 13.22 MB
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04_components-of-latent-dirichlet-allocation-model.mp4 - 10.6 MB
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01_slides-presented-in-this-module_LDA-annotated.pdf - 6.95 MB
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05_goal-of-lda-inference.en.srt - 6.72 KB
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03_an-alternative-document-clustering-model.en.srt - 6.02 KB
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02_mixed-membership-models-for-documents.en.srt - 4.76 KB
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05_goal-of-lda-inference.en.txt - 4.37 KB
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03_an-alternative-document-clustering-model.en.txt - 3.9 KB
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04_components-of-latent-dirichlet-allocation-model.en.srt - 3.28 KB
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02_mixed-membership-models-for-documents.en.txt - 3.04 KB
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04_components-of-latent-dirichlet-allocation-model.en.txt - 2 KB
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01_slides-presented-in-this-module_instructions.html - 1.13 KB
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03_collapsed-gibbs-sampling-for-lda
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03_a-worked-example-for-lda-deriving-the-resampling-distribution.mp4 - 16.69 MB
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04_using-the-output-of-collapsed-gibbs-sampling.mp4 - 15.23 MB
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01_what-is-collapsed-gibbs-sampling.mp4 - 11.71 MB
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02_a-worked-example-for-lda-initial-setup.mp4 - 8.78 MB
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03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.srt - 9.54 KB
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03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.txt - 6.09 KB
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04_using-the-output-of-collapsed-gibbs-sampling.en.srt - 5.64 KB
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02_a-worked-example-for-lda-initial-setup.en.srt - 4.9 KB
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01_what-is-collapsed-gibbs-sampling.en.srt - 4.39 KB
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04_using-the-output-of-collapsed-gibbs-sampling.en.txt - 3.55 KB
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02_a-worked-example-for-lda-initial-setup.en.txt - 3.02 KB
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01_what-is-collapsed-gibbs-sampling.en.txt - 2.88 KB
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04_summarizing-latent-dirichlet-allocation
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01_a-brief-recap.mp4 - 5.05 MB
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01_a-brief-recap.en.srt - 2.38 KB
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01_a-brief-recap.en.txt - 1.5 KB
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02_nearest-neighbor-search
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03_programming-assignment-1
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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_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_tf_idf.npz.zip - 50.92 MB
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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_word_count.npz.zip - 23.19 MB
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01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_map_index_to_word.json.zip - 5.04 MB
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01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_map_index_to_word.gl.zip - 3.7 MB
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01_choosing-features-and-metrics-for-nearest-neighbor-search_stdtypes.html - 297.93 KB
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01_choosing-features-and-metrics-for-nearest-neighbor-search_graphlab.SFrame.join.html - 281.42 KB
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01_choosing-features-and-metrics-for-nearest-neighbor-search_instructions.html - 123.56 KB
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01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.TfidfVectorizer.html - 61.6 KB
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01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.CountVectorizer.html - 57.54 KB
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01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.metrics.pairwise.euclidean_distances.html - 21.74 KB
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01_choosing-features-and-metrics-for-nearest-neighbor-search_CLU02-NB01.ipynb.zip - 7.61 KB
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06_programming-assignment-2
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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.gl.zip - 55.57 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_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_map_index_to_word.json.zip - 5.04 MB
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01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_map_index_to_word.gl.zip - 3.7 MB
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01_implementing-locality-sensitive-hashing-from-scratch_instructions.html - 328.03 KB
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01_implementing-locality-sensitive-hashing-from-scratch_itertools.html - 161.25 KB
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01_implementing-locality-sensitive-hashing-from-scratch_sklearn.feature_extraction.text.TfidfVectorizer.html - 61.6 KB
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01_implementing-locality-sensitive-hashing-from-scratch_CLU02-NB02.ipynb.zip - 10.09 KB
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05_locality-sensitive-hashing-for-approximate-nn-search
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07_optional-improving-efficiency-through-multiple-tables.mp4 - 54.2 MB
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05_searching-neighboring-bins.mp4 - 22.02 MB
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03_using-random-lines-to-partition-points.mp4 - 17.06 MB
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02_lsh-as-an-alternative-to-kd-trees.mp4 - 12.85 MB
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01_limitations-of-kd-trees.mp4 - 11.58 MB
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04_defining-more-bins.mp4 - 10.54 MB
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06_lsh-in-higher-dimensions.mp4 - 9.9 MB
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07_optional-improving-efficiency-through-multiple-tables.en.srt - 25.96 KB
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07_optional-improving-efficiency-through-multiple-tables.en.txt - 15.95 KB
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05_searching-neighboring-bins.en.srt - 10.59 KB
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03_using-random-lines-to-partition-points.en.srt - 7.82 KB
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05_searching-neighboring-bins.en.txt - 6.8 KB
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02_lsh-as-an-alternative-to-kd-trees.en.srt - 6.05 KB
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01_limitations-of-kd-trees.en.srt - 5.22 KB
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06_lsh-in-higher-dimensions.en.srt - 5.19 KB
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03_using-random-lines-to-partition-points.en.txt - 4.98 KB
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04_defining-more-bins.en.srt - 4.15 KB
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02_lsh-as-an-alternative-to-kd-trees.en.txt - 3.9 KB
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01_limitations-of-kd-trees.en.txt - 3.31 KB
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06_lsh-in-higher-dimensions.en.txt - 3.27 KB
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04_defining-more-bins.en.txt - 2.63 KB
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04_scaling-up-k-nn-search-using-kd-trees
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06_approximate-k-nn-search-using-kd-trees.mp4 - 22.95 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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01_complexity-of-brute-force-search.mp4 - 5.92 MB
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07_optional-a-worked-out-example-for-kd-trees_instructions.html - 1.26 MB
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02_kd-tree-representation.en.srt - 10.16 KB
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06_approximate-k-nn-search-using-kd-trees.en.srt - 9.76 KB
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03_nn-search-with-kd-trees.en.srt - 8.47 KB
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04_complexity-of-nn-search-with-kd-trees.en.srt - 6.46 KB
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02_kd-tree-representation.en.txt - 6.3 KB
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06_approximate-k-nn-search-using-kd-trees.en.txt - 6.18 KB
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05_visualizing-scaling-behavior-of-kd-trees.en.srt - 5.38 KB
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03_nn-search-with-kd-trees.en.txt - 4.85 KB
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04_complexity-of-nn-search-with-kd-trees.en.txt - 4.01 KB
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05_visualizing-scaling-behavior-of-kd-trees.en.txt - 3.43 KB
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01_complexity-of-brute-force-search.en.srt - 2.67 KB
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01_complexity-of-brute-force-search.en.txt - 1.61 KB
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02_the-importance-of-data-representations-and-distance-metrics
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05_to-normalize-or-not-and-other-distance-considerations.mp4 - 21.58 MB
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02_distance-metrics-euclidean-and-scaled-euclidean.mp4 - 21.05 MB
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04_distance-metrics-cosine-similarity.mp4 - 20.12 MB
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01_document-representation.mp4 - 14.08 MB
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03_writing-scaled-euclidean-distance-using-weighted-inner-products.mp4 - 8.82 MB
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05_to-normalize-or-not-and-other-distance-considerations.en.srt - 9.93 KB
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04_distance-metrics-cosine-similarity.en.srt - 9.84 KB
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02_distance-metrics-euclidean-and-scaled-euclidean.en.srt - 9.24 KB
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01_document-representation.en.srt - 7.49 KB
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05_to-normalize-or-not-and-other-distance-considerations.en.txt - 6.41 KB
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04_distance-metrics-cosine-similarity.en.txt - 6.15 KB
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02_distance-metrics-euclidean-and-scaled-euclidean.en.txt - 6.06 KB
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01_document-representation.en.txt - 4.68 KB
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03_writing-scaled-euclidean-distance-using-weighted-inner-products.en.srt - 4.4 KB
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03_writing-scaled-euclidean-distance-using-weighted-inner-products.en.txt - 2.58 KB
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01_introduction-to-nearest-neighbor-search-and-algorithms
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04_k-nn-algorithm.mp4 - 17.38 MB
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01_slides-presented-in-this-module_retrieval-intro-annotated.pdf - 13.56 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.en.srt - 8.13 KB
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04_k-nn-algorithm.en.txt - 5.12 KB
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02_retrieval-as-k-nearest-neighbor-search.en.srt - 4.41 KB
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03_1-nn-algorithm.en.srt - 3.65 KB
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02_retrieval-as-k-nearest-neighbor-search.en.txt - 2.73 KB
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03_1-nn-algorithm.en.txt - 2.33 KB
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01_slides-presented-in-this-module_instructions.html - 1.14 KB
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07_summarizing-nearest-neighbor-search
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01_a-brief-recap.mp4 - 6.37 MB
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01_a-brief-recap.en.srt - 3.95 KB
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01_a-brief-recap.en.txt - 2.43 KB
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03_clustering-with-k-means
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03_programming-assignment
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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.gl.zip - 55.57 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_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_map_index_to_word.json.zip - 5.04 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_instructions.html - 78.83 KB
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01_clustering-text-data-with-k-means_sklearn.feature_extraction.text.TfidfVectorizer.html - 61.6 KB
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01_clustering-text-data-with-k-means_sklearn.cluster.KMeans.html - 60.77 KB
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01_clustering-text-data-with-k-means_sklearn.preprocessing.normalize.html - 18.16 KB
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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_numpy.mean.html - 12.24 KB
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01_clustering-text-data-with-k-means_numpy.argmin.html - 9.29 KB
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01_clustering-text-data-with-k-means_pyplot_api.html - 330 B
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05_summarizing-clustering-with-k-means
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01_other-applications-of-clustering.mp4 - 21.55 MB
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02_a-brief-recap.mp4 - 3.73 MB
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01_other-applications-of-clustering.en.srt - 9.68 KB
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01_other-applications-of-clustering.en.txt - 6.13 KB
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02_a-brief-recap.en.srt - 1.88 KB
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02_a-brief-recap.en.txt - 1.16 KB
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02_clustering-via-k-means
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04_assessing-the-quality-and-choosing-the-number-of-clusters.mp4 - 21.18 MB
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02_k-means-as-coordinate-descent.mp4 - 17.76 MB
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01_the-k-means-algorithm.mp4 - 17.25 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.en.srt - 11.04 KB
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02_k-means-as-coordinate-descent.en.srt - 8.42 KB
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01_the-k-means-algorithm.en.srt - 7.91 KB
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04_assessing-the-quality-and-choosing-the-number-of-clusters.en.txt - 6.74 KB
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02_k-means-as-coordinate-descent.en.txt - 5.34 KB
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03_smart-initialization-via-k-means.en.srt - 5.26 KB
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01_the-k-means-algorithm.en.txt - 4.87 KB
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04_mapreduce-for-scaling-k-means
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01_motivating-mapreduce.mp4 - 20.39 MB
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04_mapreduce-for-k-means.mp4 - 18.54 MB
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03_mapreduce-execution-overview-and-combiners.mp4 - 14.42 MB
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02_the-general-mapreduce-abstraction.mp4 - 12.11 MB
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01_motivating-mapreduce.en.srt - 10.28 KB
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04_mapreduce-for-k-means.en.srt - 8.04 KB
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03_mapreduce-execution-overview-and-combiners.en.srt - 6.64 KB
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01_motivating-mapreduce.en.txt - 6.28 KB
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03_coordinate-descent-for-lasso-unnormalized-features.en.srt - 2.39 KB
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08_programming-assignment-1
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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_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_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_instructions.html - 13.76 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_numpy-tutorial-py3.ipynb.zip - 2.92 KB
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09_programming-assignment-2
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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_train_data.csv.zip - 628.45 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_instructions.html - 19.62 KB
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01_implementing-lasso-using-coordinate-descent_REG05-NB02.ipynb.zip - 5.66 KB
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01_implementing-lasso-using-coordinate-descent_numpy-tutorial-py3.ipynb.zip - 2.92 KB
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03_multiple-regression
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01_multiple-features-of-one-input
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04_modeling-seasonality.mp4 - 30.01 MB
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03_polynomial-regression.mp4 - 11.56 MB
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05_where-we-see-seasonality.mp4 - 11.26 MB
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01_slides-presented-in-this-module_week2_multipleregression-annotated.pdf - 10.12 MB
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06_regression-with-general-features-of-1-input.mp4 - 8.6 MB
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02_multiple-regression-intro.mp4 - 1.21 MB
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04_modeling-seasonality.en.srt - 10.61 KB
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04_modeling-seasonality.en.txt - 6.75 KB
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03_polynomial-regression.en.srt - 5.23 KB
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05_where-we-see-seasonality.en.srt - 4.76 KB
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03_polynomial-regression.en.txt - 3.35 KB
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06_regression-with-general-features-of-1-input.en.srt - 3.09 KB
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05_where-we-see-seasonality.en.txt - 3.08 KB
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06_regression-with-general-features-of-1-input.en.txt - 1.94 KB
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01_slides-presented-in-this-module_instructions.html - 1.15 KB
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02_multiple-regression-intro.en.srt - 643 B
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02_multiple-regression-intro.en.txt - 380 B
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02_incorporating-multiple-inputs
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04_interpreting-the-multiple-regression-fit.mp4 - 24.67 MB
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01_motivating-the-use-of-multiple-inputs.mp4 - 14.17 MB
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03_regression-with-features-of-multiple-inputs.mp4 - 12.6 MB
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02_defining-notation.mp4 - 11.87 MB
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04_interpreting-the-multiple-regression-fit.en.srt - 10.06 KB
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01_motivating-the-use-of-multiple-inputs.en.srt - 6 KB
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03_regression-with-features-of-multiple-inputs.en.srt - 4.92 KB
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02_defining-notation.en.srt - 4.34 KB
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01_motivating-the-use-of-multiple-inputs.en.txt - 3.92 KB
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03_regression-with-features-of-multiple-inputs.en.txt - 3.07 KB
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02_defining-notation.en.txt - 2.73 KB
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03_setting-the-stage-for-computing-the-least-squares-fit
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04_computing-the-cost-of-a-d-dimensional-curve.mp4 - 21.24 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.en.srt - 10.11 KB
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02_rewriting-the-single-observation-model-in-vector-notation.en.srt - 7.8 KB
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04_computing-the-cost-of-a-d-dimensional-curve.en.txt - 6.05 KB
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03_rewriting-the-model-for-all-observations-in-matrix-notation.en.srt - 4.82 KB
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02_rewriting-the-single-observation-model-in-vector-notation.en.txt - 4.78 KB
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01_optional-reading-review-of-matrix-algebra_instructions.html - 1.12 KB
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04_computing-the-least-squares-d-dimensional-curve
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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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03_discussing-the-closed-form-solution.mp4 - 10.85 MB
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02_approach-1-closed-form-solution.mp4 - 9.99 MB
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01_computing-the-gradient-of-rss.mp4 - 7.25 MB
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04_approach-2-gradient-descent.mp4 - 5.36 MB
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05_feature-by-feature-update.en.srt - 8.74 KB
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06_algorithmic-summary-of-gradient-descent-approach.en.srt - 5.03 KB
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02_approach-1-closed-form-solution.en.srt - 4.39 KB
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05_summarizing-multiple-regression
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01_a-brief-recap.mp4 - 3.91 MB
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01_a-brief-recap.en.txt - 1.01 KB
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06_programming-assignment-1
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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_kc_house_train_data.csv.zip - 628.45 KB
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01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_test_data.csv.zip - 154.8 KB
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01_exploring-different-multiple-regression-models-for-house-price-prediction_instructions.html - 10.02 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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07_programming-assignment-2
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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_train_data.csv.zip - 628.45 KB
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01_numpy-tutorial_quickstart.html - 162.86 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_instructions.html - 14.94 KB
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02_implementing-gradient-descent-for-multiple-regression_REG02-NB02.ipynb.zip - 5.65 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_numpy-tutorial-py3.ipynb.zip.ipynb - 2.92 KB
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01_numpy-tutorial_instructions.html - 2.22 KB
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05_ridge-regression
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02_the-ridge-objective
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05_ridge-regression-demo.mp4 - 24.95 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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06_the-ridge-coefficient-path.mp4 - 9.57 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.en.srt - 12.01 KB
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01_balancing-fit-and-magnitude-of-coefficients.en.srt - 7.61 KB
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05_ridge-regression-demo.en.txt - 7.54 KB
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02_the-resulting-ridge-objective-and-its-extreme-solutions.en.srt - 6.34 KB
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01_balancing-fit-and-magnitude-of-coefficients.en.txt - 4.67 KB
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03_how-ridge-regression-balances-bias-and-variance.en.srt - 1.99 KB
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03_how-ridge-regression-balances-bias-and-variance.en.txt - 1.26 KB
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03_optimizing-the-ridge-objective
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04_approach-2-gradient-descent.mp4 - 22.08 MB
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01_computing-the-gradient-of-the-ridge-objective.mp4 - 14.78 MB
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03_discussing-the-closed-form-solution.mp4 - 13.45 MB
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02_approach-1-closed-form-solution.mp4 - 13.31 MB
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04_approach-2-gradient-descent.en.srt - 10.05 KB
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02_approach-1-closed-form-solution.en.srt - 6.58 KB
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03_discussing-the-closed-form-solution.en.srt - 6.19 KB
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01_computing-the-gradient-of-the-ridge-objective.en.srt - 5.73 KB
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02_approach-1-closed-form-solution.en.txt - 3.9 KB
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01_computing-the-gradient-of-the-ridge-objective.en.txt - 3.58 KB
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04_tying-up-the-loose-ends
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03_how-to-handle-the-intercept.mp4 - 18.6 MB
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02_k-fold-cross-validation.mp4 - 17.01 MB
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01_selecting-tuning-parameters-via-cross-validation.mp4 - 12.39 MB
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04_a-brief-recap.mp4 - 5.24 MB
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03_how-to-handle-the-intercept.en.srt - 7.61 KB
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02_k-fold-cross-validation.en.srt - 7.29 KB
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01_selecting-tuning-parameters-via-cross-validation.en.srt - 5.19 KB
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03_how-to-handle-the-intercept.en.txt - 4.69 KB
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02_k-fold-cross-validation.en.txt - 4.68 KB
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01_selecting-tuning-parameters-via-cross-validation.en.txt - 3.25 KB
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01_characteristics-of-overfit-models
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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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02_symptoms-of-overfitting-in-polynomial-regression.mp4 - 6.91 MB
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01_slides-presented-in-this-module_week4_ridgeregression-annotated.pdf - 3.01 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.en.srt - 8.52 KB
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04_overfitting-demo.en.txt - 5.34 KB
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05_overfitting-for-more-general-multiple-regression-models.en.srt - 5.05 KB
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02_symptoms-of-overfitting-in-polynomial-regression.en.srt - 3.03 KB
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03_download-the-notebook-and-follow-along_instructions.html - 1.42 KB
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01_slides-presented-in-this-module_instructions.html - 1.15 KB
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05_programming-assignment-1
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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_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_train_data.csv.zip - 354.68 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_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_1_data.csv.zip - 158.64 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_set_2_data.csv.zip - 157.64 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_instructions.html - 19.77 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_numpy-tutorial-py3.ipynb.zip - 2.92 KB
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06_programming-assignment-2
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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_train_data.csv.zip - 628.45 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_instructions.html - 18.59 KB
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01_implementing-ridge-regression-via-gradient-descent_REG04-NB02.ipynb.zip - 4.71 KB
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01_implementing-ridge-regression-via-gradient-descent_numpy-tutorial-py3.ipynb.zip - 2.92 KB
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02_simple-linear-regression
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01_regression-fundamentals
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03_regression-fundamentals-data-model.mp4 - 22.35 MB
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05_regression-ml-block-diagram.mp4 - 11.74 MB
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01_slides-presented-in-this-module_week1_simpleregression-annotated.pdf - 7.94 MB
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04_regression-fundamentals-the-task.mp4 - 7.33 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.en.srt - 9.74 KB
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03_regression-fundamentals-data-model.en.txt - 5.95 KB
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04_regression-fundamentals-the-task.en.srt - 3.13 KB
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06_discussion-and-summary-of-simple-linear-regression
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03_influence-of-high-leverage-points-removing-center-city.mp4 - 19.52 MB
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02_influence-of-high-leverage-points-exploring-the-data.mp4 - 11.92 MB
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05_asymmetric-cost-functions.mp4 - 9.4 MB
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04_influence-of-high-leverage-points-removing-high-end-towns.mp4 - 9.33 MB
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06_a-brief-recap.mp4 - 4.48 MB
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01_download-notebooks-to-follow-along_PhillyCrime.ipynb.zip - 44.94 KB
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03_influence-of-high-leverage-points-removing-center-city.en.srt - 9.18 KB
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03_influence-of-high-leverage-points-removing-center-city.en.txt - 5.7 KB
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02_influence-of-high-leverage-points-exploring-the-data.en.srt - 5.66 KB
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05_asymmetric-cost-functions.en.srt - 4.36 KB
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04_influence-of-high-leverage-points-removing-high-end-towns.en.srt - 3.92 KB
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02_influence-of-high-leverage-points-exploring-the-data.en.txt - 3.58 KB
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05_asymmetric-cost-functions.en.txt - 2.65 KB
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04_influence-of-high-leverage-points-removing-high-end-towns.en.txt - 2.5 KB
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01_download-notebooks-to-follow-along_Philadelphia_Crime_Rate_noNA.csv.zip - 2.12 KB
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01_download-notebooks-to-follow-along_instructions.html - 1.79 KB
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05_finding-the-least-squares-line
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04_approach-2-gradient-descent.mp4 - 18.36 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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06_comparing-the-approaches.mp4 - 6.22 MB
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05_optional-reading-worked-out-example-for-gradient-descent_instructions.html - 29.52 KB
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03_optional-reading-worked-out-example-for-closed-form-solution_instructions.html - 12.45 KB
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01_computing-the-gradient-of-rss.en.srt - 8.31 KB
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04_approach-2-gradient-descent.en.srt - 6.93 KB
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02_approach-1-closed-form-solution.en.srt - 5.49 KB
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02_the-simple-linear-regression-model-its-use-and-interpretation
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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_the-simple-linear-regression-model.mp4 - 7.65 MB
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02_the-cost-of-using-a-given-line.en.srt - 7.87 KB
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04_an-aside-on-optimization-multidimensional-objectives
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02_gradient-descent-multidimensional-hill-descent.mp4 - 16.87 MB
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01_gradients-derivatives-in-multiple-dimensions.mp4 - 14.01 MB
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03_an-aside-on-optimization-one-dimensional-objectives
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04_finding-the-max-via-hill-climbing.mp4 - 15.69 MB
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06_choosing-stepsize-and-convergence-criteria.mp4 - 14.71 MB
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01_defining-our-least-squares-optimization-objective.mp4 - 10.19 MB
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05_finding-the-min-via-hill-descent.mp4 - 8.54 MB
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03_maximizing-a-1d-function-a-worked-example.mp4 - 7.33 MB
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07_programming-assignment
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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_train_data.csv.zip - 628.45 KB
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01_welcome
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01_what-is-this-course-about
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ml-classification
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02_linear-classifiers-logistic-regression
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06_programming-assignment
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06_programming-assignment
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01_logistic-regression-with-l2-regularization_amazon_baby_subset.sframe.zip - 12.73 MB
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01_logistic-regression-with-l2-regularization_amazon_baby_subset.gl.zip - 12.73 MB
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01_logistic-regression-with-l2-regularization_amazon_baby_subset.csv.zip - 9.16 MB
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01_logistic-regression-with-l2-regularization_module-4-assignment-numpy-arrays.npz.zip - 1.16 MB
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01_logistic-regression-with-l2-regularization_module-4-assignment-train-idx.json.zip - 47.14 KB
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07_programming-assignment-2
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04_programming-assignment-1
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01_exploring-ensemble-methods_lending-club-data.gl.zip - 19.38 MB
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01_exploring-ensemble-methods_boosted_trees_classifier.html - 283.64 KB
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01_exploring-ensemble-methods_sklearn.ensemble.GradientBoostingClassifier.html - 83.4 KB
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01_exploring-ensemble-methods_module-8-assignment-1-validation-idx.json.zip - 18.93 KB
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06_summarizing-boosting
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01_ensemble-methods-impact-of-boosting-quick-recap.mp4 - 16.2 MB
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05_convergence-and-overfitting-in-boosting
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01_the-boosting-theorem.mp4 - 10.85 MB
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02_overfitting-in-boosting.en.srt - 6.93 KB
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03_applying-adaboost
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02_learning-boosted-decision-stumps-with-adaboost.mp4 - 13.99 MB
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01_example-of-adaboost-in-action.mp4 - 11.87 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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06_programming-assignment-1
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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_lending-club-data.csv.zip - 18.53 MB
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01_identifying-safe-loans-with-decision-trees_graphlab.SFrame.to_numpy.html - 281.42 KB
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01_identifying-safe-loans-with-decision-trees_sklearn.tree.DecisionTreeClassifier.html - 74.9 KB
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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_instructions.html - 25.01 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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07_programming-assignment-2
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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_lending-club-data.csv.zip - 18.53 MB
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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_instructions.html - 30.75 KB
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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_CLA05-NB02.ipynb.zip - 8.24 KB
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01_threshold-splits-for-continuous-inputs.mp4 - 17.28 MB
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03_visualizing-decision-boundaries.mp4 - 13.2 MB
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02_optional-picking-the-best-threshold-to-split-on.mp4 - 9.47 MB
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03_selecting-best-feature-to-split-on.mp4 - 15.14 MB
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02_learning-a-decision-stump.mp4 - 12.9 MB
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01_recursive-greedy-algorithm.mp4 - 11.15 MB
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03_intuition-behind-decision-trees.mp4 - 5.9 MB
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01_slides-presented-in-this-module_decision-trees-annotated.pdf - 2.85 MB
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03_learning-linear-classifiers
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03_example-of-computing-derivative-for-logistic-regression.mp4 - 13.75 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.csv.zip - 9.16 MB
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01_implementing-logistic-regression-from-scratch_module-3-assignment-numpy-arrays.npz.zip - 1.16 MB
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01_implementing-logistic-regression-from-scratch_instructions.html - 30.78 KB
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01_implementing-logistic-regression-from-scratch_CLA03-NB01.ipynb.zip - 6.92 KB
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01_modifying-decision-trees-to-handle-missing-data.mp4 - 14.41 MB
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03_strategy-1-purification-by-skipping-missing-data.mp4 - 12.92 MB
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02_challenge-of-missing-data.mp4 - 12.63 MB
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01_slides-presented-in-this-module_decision-trees-missing-values-annotated.pdf - 1.57 MB
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10_scaling-to-huge-datasets-online-learning
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07_programming-assignment
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01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.sframe.zip - 12.73 MB
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01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.gl.zip - 12.73 MB
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01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.csv.zip - 9.16 MB
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[TGx]Downloaded from torrentgalaxy.to .txt - 585 B
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TutsNode.net.txt - 63 B
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| Trackers | Seeders | Leechers | Downloads |
|---|---|---|---|
| udp://tracker2.dler.com:80/announce | 0 | 0 | 0 |
| udp://tracker.torrent.eu.org:451/announce | 18 | 3 | 873 |
| udp://www.torrent.eu.org:451/announce | 18 | 3 | 864 |
| udp://tracker.leech.ie:1337/announce | 0 | 0 | 0 |
| udp://tracker.opentrackr.org:1337/announce | 20 | 6 | 1157 |
| udp://pow7.com:80/announce | 8 | 1 | 10 |
| udp://open.stealth.si:80/announce | 19 | 6 | 172 |
| udp://p4p.arenabg.com:1337/announce | 9 | 2 | 0 |
| udp://movies.zsw.ca:6969/announce | 0 | 0 | 27 |
| udp://tracker.ololosh.space:6969/announce | 1 | 1 | 0 |
| udp://opentracker.i2p.rocks:6969/announce | 13 | 4 | 942 |
| udp://exodus.desync.com:6969/announce | 3 | 0 | 0 |
| udp://tracker.theoks.net:6969/announce | 0 | 0 | 31 |
| udp://bt2.archive.org:6969/announce | 0 | 0 | 0 |
| udp://bt1.archive.org:6969/announce | 0 | 0 | 0 |
| udp://explodie.org:6969/announce | 7 | 1 | 0 |
| udp://tracker.army:6969/announce | 0 | 0 | 0 |
| udp://tracker.dler.com:6969/announce | 1 | 0 | 0 |
| udp://tracker1.bt.moack.co.kr:80/announce | 0 | 0 | 0 |
InfoHash:
1C6FE20E31AFAB2F041D8BC4299B968B95EEE660
Last updated:
Aug 17, 2023
File count:
2.81 KB
File Category:
9
Tags:
Other, Tutorials