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Machine Learning Specialization
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[TutsNode.net] - Machine Learning Specialization (Size: 13.16 GB) (Files: 2880)
[TutsNode.net] - Machine Learning Specialization
advanced-learning-algorithms
04_decision-trees
04_conversations-with-andrew-optional
01_andrew-ng-and-chris-manning-on-natural-language-processing.mp4
01_andrew-ng-and-chris-manning-on-natural-language-processing.en.txt
01_andrew-ng-and-chris-manning-on-natural-language-processing.en.srt
01_decision-trees
02_learning-process.en.srt
01_decision-tree-model.en.srt
02_learning-process.en.txt
01_decision-tree-model.en.txt
02_learning-process.mp4
01_decision-tree-model.mp4
02_decision-tree-learning
02_choosing-a-split-information-gain.en.srt
03_putting-it-together.en.srt
06_regression-trees-optional.en.srt
01_measuring-purity.en.srt
02_choosing-a-split-information-gain.en.txt
05_continuous-valued-features.en.srt
03_putting-it-together.en.txt
06_regression-trees-optional.en.txt
04_using-one-hot-encoding-of-categorical-features.en.srt
05_continuous-valued-features.en.txt
01_measuring-purity.en.txt
04_using-one-hot-encoding-of-categorical-features.en.txt
02_choosing-a-split-information-gain.mp4
06_regression-trees-optional.mp4
03_putting-it-together.mp4
01_measuring-purity.mp4
05_continuous-valued-features.mp4
04_using-one-hot-encoding-of-categorical-features.mp4
03_tree-ensembles
05_when-to-use-decision-trees.en.srt
04_xgboost.en.srt
03_random-forest-algorithm.en.srt
02_sampling-with-replacement.en.srt
01_using-multiple-decision-trees.en.srt
04_xgboost.en.txt
05_when-to-use-decision-trees.en.txt
03_random-forest-algorithm.en.txt
02_sampling-with-replacement.en.txt
01_using-multiple-decision-trees.en.txt
04_xgboost.mp4
05_when-to-use-decision-trees.mp4
02_sampling-with-replacement.mp4
03_random-forest-algorithm.mp4
01_using-multiple-decision-trees.mp4
05_acknowledgments
01_acknowledgements_instructions.html
01_neural-networks
01_neural-networks-intuition
01_welcome.en.txt
03_demand-prediction.en.srt
02_neurons-and-the-brain.en.srt
03_demand-prediction.en.txt
04_example-recognizing-images.en.srt
02_neurons-and-the-brain.en.txt
01_welcome.en.srt
04_example-recognizing-images.en.txt
02_neurons-and-the-brain.mp4
03_demand-prediction.mp4
04_example-recognizing-images.mp4
01_welcome.mp4
05_speculations-on-artificial-general-intelligence-agi
01_is-there-a-path-to-agi.en.srt
01_is-there-a-path-to-agi.en.txt
01_is-there-a-path-to-agi.mp4
03_tensorflow-implementation
02_data-in-tensorflow.en.srt
03_building-a-neural-network.en.srt
01_inference-in-code.en.srt
02_data-in-tensorflow.en.txt
03_building-a-neural-network.en.txt
01_inference-in-code.en.txt
02_data-in-tensorflow.mp4
03_building-a-neural-network.mp4
01_inference-in-code.mp4
02_neural-network-model
01_neural-network-layer.en.srt
02_more-complex-neural-networks.en.srt
03_inference-making-predictions-forward-propagation.en.srt
01_neural-network-layer.en.txt
02_more-complex-neural-networks.en.txt
03_inference-making-predictions-forward-propagation.en.txt
01_neural-network-layer.mp4
02_more-complex-neural-networks.mp4
03_inference-making-predictions-forward-propagation.mp4
06_vectorization-optional
02_matrix-multiplication.en.srt
03_matrix-multiplication-rules.en.srt
04_matrix-multiplication-code.en.srt
03_matrix-multiplication-rules.en.txt
02_matrix-multiplication.en.txt
01_how-neural-networks-are-implemented-efficiently.en.srt
04_matrix-multiplication-code.en.txt
01_how-neural-networks-are-implemented-efficiently.en.txt
03_matrix-multiplication-rules.mp4
02_matrix-multiplication.mp4
04_matrix-multiplication-code.mp4
01_how-neural-networks-are-implemented-efficiently.mp4
04_neural-network-implementation-in-python
02_general-implementation-of-forward-propagation.en.srt
01_forward-prop-in-a-single-layer.en.srt
02_general-implementation-of-forward-propagation.en.txt
01_forward-prop-in-a-single-layer.en.txt
02_general-implementation-of-forward-propagation.mp4
01_forward-prop-in-a-single-layer.mp4
02_neural-network-training
03_multiclass-classification
01_multiclass.en.txt
02_softmax.en.srt
04_improved-implementation-of-softmax.en.srt
03_neural-network-with-softmax-output.en.srt
02_softmax.en.txt
04_improved-implementation-of-softmax.en.txt
05_classification-with-multiple-outputs-optional.en.srt
03_neural-network-with-softmax-output.en.txt
01_multiclass.en.srt
05_classification-with-multiple-outputs-optional.en.txt
02_softmax.mp4
04_improved-implementation-of-softmax.mp4
03_neural-network-with-softmax-output.mp4
05_classification-with-multiple-outputs-optional.mp4
01_multiclass.mp4
05_back-propagation-optional
02_computation-graph-optional.en.srt
01_what-is-a-derivative-optional.en.txt
01_what-is-a-derivative-optional.mp4
01_what-is-a-derivative-optional.en.srt
02_computation-graph-optional.en.txt
03_larger-neural-network-example-optional.en.srt
03_larger-neural-network-example-optional.en.txt
02_computation-graph-optional.mp4
03_larger-neural-network-example-optional.mp4
01_neural-network-training
02_training-details.en.srt
02_training-details.en.txt
01_tensorflow-implementation.en.srt
01_tensorflow-implementation.en.txt
02_training-details.mp4
01_tensorflow-implementation.mp4
02_activation-functions
02_choosing-activation-functions.en.srt
03_why-do-we-need-activation-functions.en.srt
02_choosing-activation-functions.en.txt
01_alternatives-to-the-sigmoid-activation.en.srt
01_alternatives-to-the-sigmoid-activation.en.txt
03_why-do-we-need-activation-functions.en.txt
02_choosing-activation-functions.mp4
03_why-do-we-need-activation-functions.mp4
01_alternatives-to-the-sigmoid-activation.mp4
04_additional-neural-network-concepts
02_additional-layer-types.en.srt
01_advanced-optimization.en.srt
02_additional-layer-types.en.txt
01_advanced-optimization.en.txt
02_additional-layer-types.mp4
01_advanced-optimization.mp4
03_advice-for-applying-machine-learning
01_advice-for-applying-machine-learning
03_model-selection-and-training-cross-validation-test-sets.en.srt
02_evaluating-a-model.en.srt
03_model-selection-and-training-cross-validation-test-sets.en.txt
02_evaluating-a-model.en.txt
01_deciding-what-to-try-next.en.srt
01_deciding-what-to-try-next.en.txt
03_model-selection-and-training-cross-validation-test-sets.mp4
02_evaluating-a-model.mp4
01_deciding-what-to-try-next.mp4
03_machine-learning-development-process
04_transfer-learning-using-data-from-a-different-task.en.srt
03_adding-data.en.srt
05_full-cycle-of-a-machine-learning-project.en.srt
06_fairness-bias-and-ethics.en.srt
02_error-analysis.en.srt
01_iterative-loop-of-ml-development.en.srt
03_adding-data.en.txt
04_transfer-learning-using-data-from-a-different-task.en.txt
06_fairness-bias-and-ethics.en.txt
03_adding-data.mp4
05_full-cycle-of-a-machine-learning-project.en.txt
02_error-analysis.en.txt
01_iterative-loop-of-ml-development.en.txt
06_fairness-bias-and-ethics.mp4
04_transfer-learning-using-data-from-a-different-task.mp4
02_error-analysis.mp4
05_full-cycle-of-a-machine-learning-project.mp4
01_iterative-loop-of-ml-development.mp4
02_bias-and-variance
04_learning-curves.en.srt
01_diagnosing-bias-and-variance.en.srt
02_regularization-and-bias-variance.en.srt
03_establishing-a-baseline-level-of-performance.en.srt
06_bias-variance-and-neural-networks.en.srt
05_deciding-what-to-try-next-revisited.en.srt
04_learning-curves.en.txt
06_bias-variance-and-neural-networks.en.txt
01_diagnosing-bias-and-variance.en.txt
02_regularization-and-bias-variance.en.txt
03_establishing-a-baseline-level-of-performance.en.txt
05_deciding-what-to-try-next-revisited.en.txt
05_deciding-what-to-try-next-revisited.mp4
06_bias-variance-and-neural-networks.mp4
04_learning-curves.mp4
02_regularization-and-bias-variance.mp4
01_diagnosing-bias-and-variance.mp4
03_establishing-a-baseline-level-of-performance.mp4
04_skewed-datasets-optional
02_trading-off-precision-and-recall.en.srt
01_error-metrics-for-skewed-datasets.en.srt
02_trading-off-precision-and-recall.en.txt
01_error-metrics-for-skewed-datasets.en.txt
02_trading-off-precision-and-recall.mp4
01_error-metrics-for-skewed-datasets.mp4
ml-foundations
06_deep-learning-searching-for-images
01_neural-networks-learning-very-non-linear-features
01_slides-presented-in-this-module_LM-3dtexton.pdf
01_slides-presented-in-this-module_eccv06.pdf
02_searching-for-images-a-case-study-in-deep-learning.mp4
01_slides-presented-in-this-module_instructions.html
01_slides-presented-in-this-module_imagenet.pdf
01_slides-presented-in-this-module_iccv99.pdf
01_slides-presented-in-this-module_Dalal-cvpr05.pdf
04_learning-very-non-linear-features-with-neural-networks.en.srt
04_learning-very-non-linear-features-with-neural-networks.en.txt
04_learning-very-non-linear-features-with-neural-networks.mp4
03_what-is-a-visual-product-recommender.en.srt
03_what-is-a-visual-product-recommender.en.txt
02_searching-for-images-a-case-study-in-deep-learning.en.srt
02_searching-for-images-a-case-study-in-deep-learning.en.txt
03_what-is-a-visual-product-recommender.mp4
01_slides-presented-in-this-module_deeplearning-annotated.pdf
01_slides-presented-in-this-module_johnson_andrew_1997_3.pdf
01_slides-presented-in-this-module_mikolajczyk_pami05.pdf
03_summary-of-deep-learning
02_deep-learning_exam.html
01_deep-learning-ml-block-diagram.en.srt
01_deep-learning-ml-block-diagram.en.txt
01_deep-learning-ml-block-diagram.mp4
06_programming-assignment
01_deep-features-for-image-retrieval-assignment_nearest_neighbors.html
01_deep-features-for-image-retrieval-assignment_image_test_data.csv
01_deep-features-for-image-retrieval-assignment_tabular-data.html
01_deep-features-for-image-retrieval-assignment_instructions.html
01_deep-features-for-image-retrieval-assignment_turicreate.Sketch.html
01_deep-features-for-image-retrieval-assignment_image_train_data.csv
01_deep-features-for-image-retrieval-assignment_nearest_neighbors.md
01_deep-features-for-image-retrieval-assignment_image_test_data.zip
02_deep-features-for-image-retrieval_exam.html
01_deep-features-for-image-retrieval-assignment_image_train_data.zip
04_deep-features-for-image-classification-jupyter-notebook
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_test_data.zip
04_training-evaluating-a-classifier-using-deep-features.en.srt
03_training-evaluating-a-classifier-using-raw-image-pixels.en.srt
04_training-evaluating-a-classifier-using-deep-features.en.txt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND06-NB01.ipynb.zip
02_loading-image-data.en.srt
03_training-evaluating-a-classifier-using-raw-image-pixels.en.txt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html
02_loading-image-data.en.txt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_train_data.zip
04_training-evaluating-a-classifier-using-deep-features.mp4
03_training-evaluating-a-classifier-using-raw-image-pixels.mp4
02_loading-image-data.mp4
05_deep-features-for-image-retrieval-jupyter-notebook
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_test_data.zip
05_querying-for-the-most-similar-images-for-car-image.en.srt
04_querying-the-nearest-neighbors-model-to-retrieve-images.en.srt
06_displaying-other-example-image-retrievals-with-a-python-lambda.en.srt
04_querying-the-nearest-neighbors-model-to-retrieve-images.en.txt
02_loading-image-data.en.srt
06_displaying-other-example-image-retrievals-with-a-python-lambda.en.txt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND06-NB02.ipynb.zip
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html
02_loading-image-data.en.txt
03_creating-a-nearest-neighbors-model-for-image-retrieval.en.srt
03_creating-a-nearest-neighbors-model-for-image-retrieval.en.txt
05_querying-for-the-most-similar-images-for-car-image.en.txt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_train_data.zip
04_querying-the-nearest-neighbors-model-to-retrieve-images.mp4
06_displaying-other-example-image-retrievals-with-a-python-lambda.mp4
02_loading-image-data.mp4
03_creating-a-nearest-neighbors-model-for-image-retrieval.mp4
05_querying-for-the-most-similar-images-for-car-image.mp4
02_deep-learning-deep-features
06_deep-features.en.srt
01_application-of-deep-learning-to-computer-vision.en.srt
06_deep-features.en.txt
01_application-of-deep-learning-to-computer-vision.en.txt
03_demo-of-deep-learning-model-on-imagenet-data.en.srt
02_deep-learning-performance.en.srt
05_challenges-of-deep-learning.en.srt
02_deep-learning-performance.en.txt
03_demo-of-deep-learning-model-on-imagenet-data.en.txt
04_other-examples-of-deep-learning-in-computer-vision.en.srt
04_other-examples-of-deep-learning-in-computer-vision.en.txt
05_challenges-of-deep-learning.en.txt
06_deep-features.mp4
01_application-of-deep-learning-to-computer-vision.mp4
02_deep-learning-performance.mp4
05_challenges-of-deep-learning.mp4
03_demo-of-deep-learning-model-on-imagenet-data.mp4
04_other-examples-of-deep-learning-in-computer-vision.mp4
05_recommending-products
07_programming-assignment
01_recommending-songs-assignment_song_data.csv
01_recommending-songs-assignment_song_data.sframe.zip
01_recommending-songs-assignment_graphlab.SFrame.groupby.html
01_recommending-songs-assignment_FND05-NB01.ipynb.zip
01_recommending-songs-assignment_instructions.html
02_recommending-songs_exam.html
05_summary-of-recommender-systems
02_recommender-systems_exam.html
01_recommender-systems-ml-block-diagram.en.txt
01_recommender-systems-ml-block-diagram.en.srt
01_recommender-systems-ml-block-diagram.mp4
06_song-recommender-jupyter-notebook
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_song_data.sframe.zip
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND05-NB01.ipynb.zip
04_creating-evaluating-a-personalized-song-recommender.en.srt
02_loading-and-exploring-song-data.en.srt
03_creating-evaluating-a-popularity-based-song-recommender.en.srt
05_using-precision-recall-to-compare-recommender-models.en.srt
04_creating-evaluating-a-personalized-song-recommender.en.txt
02_loading-and-exploring-song-data.en.txt
03_creating-evaluating-a-popularity-based-song-recommender.en.txt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html
05_using-precision-recall-to-compare-recommender-models.en.txt
04_creating-evaluating-a-personalized-song-recommender.mp4
02_loading-and-exploring-song-data.mp4
03_creating-evaluating-a-popularity-based-song-recommender.mp4
05_using-precision-recall-to-compare-recommender-models.mp4
01_recommender-systems
03_where-we-see-recommender-systems-in-action.en.srt
03_where-we-see-recommender-systems-in-action.en.txt
04_building-a-recommender-system-via-classification.en.srt
04_building-a-recommender-system-via-classification.en.txt
01_slides-presented-in-this-module_instructions.html
02_recommender-systems-overview.en.srt
02_recommender-systems-overview.en.txt
03_where-we-see-recommender-systems-in-action.mp4
04_building-a-recommender-system-via-classification.mp4
01_slides-presented-in-this-module_recommenders-intro-annotated.pdf
02_recommender-systems-overview.mp4
03_matrix-factorization
04_discovering-hidden-structure-by-matrix-factorization.en.srt
02_recommendations-from-known-user-item-features.en.srt
01_the-matrix-completion-task.en.srt
04_discovering-hidden-structure-by-matrix-factorization.en.txt
05_bringing-it-all-together-featurized-matrix-factorization.en.srt
01_the-matrix-completion-task.en.txt
02_recommendations-from-known-user-item-features.en.txt
03_predictions-in-matrix-form.en.srt
03_predictions-in-matrix-form.en.txt
05_bringing-it-all-together-featurized-matrix-factorization.en.txt
04_discovering-hidden-structure-by-matrix-factorization.mp4
01_the-matrix-completion-task.mp4
02_recommendations-from-known-user-item-features.mp4
05_bringing-it-all-together-featurized-matrix-factorization.mp4
03_predictions-in-matrix-form.mp4
04_performance-metrics-for-recommender-systems
03_precision-recall-curves.en.srt
01_a-performance-metric-for-recommender-systems.en.srt
03_precision-recall-curves.en.txt
01_a-performance-metric-for-recommender-systems.en.txt
02_optimal-recommenders.en.srt
02_optimal-recommenders.en.txt
01_a-performance-metric-for-recommender-systems.mp4
03_precision-recall-curves.mp4
02_optimal-recommenders.mp4
02_co-occurrence-matrices-for-collaborative-filtering
03_normalizing-co-occurrence-matrices-and-leveraging-purchase-histories.en.srt
01_collaborative-filtering-people-who-bought-this-also-bought.en.srt
03_normalizing-co-occurrence-matrices-and-leveraging-purchase-histories.en.txt
01_collaborative-filtering-people-who-bought-this-also-bought.en.txt
02_effect-of-popular-items.en.srt
02_effect-of-popular-items.en.txt
03_normalizing-co-occurrence-matrices-and-leveraging-purchase-histories.mp4
01_collaborative-filtering-people-who-bought-this-also-bought.mp4
02_effect-of-popular-items.mp4
04_clustering-and-similarity-retrieving-documents
05_programming-assignment
01_retrieving-wikipedia-articles-assignment_people_wiki.csv
01_retrieving-wikipedia-articles-assignment_people_wiki.sframe.zip
01_retrieving-wikipedia-articles-assignment_FND04-NB01.ipynb.zip
01_retrieving-wikipedia-articles-assignment_turicreate.toolkits.distances.cosine.html
01_retrieving-wikipedia-articles-assignment_instructions.html
02_retrieving-wikipedia-articles_exam.html
03_summary-of-clustering-and-similarity
02_clustering-and-similarity_exam.html
01_clustering-and-similarity-ml-block-diagram.en.srt
01_clustering-and-similarity-ml-block-diagram.en.txt
01_clustering-and-similarity-ml-block-diagram.mp4
04_document-retrieval-jupyter-notebook
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_people_wiki.sframe.zip
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND04-NB01.ipynb.zip
04_computing-exploring-tf-idfs.en.srt
03_exploring-word-counts.en.srt
05_computing-distances-between-wikipedia-articles.en.srt
02_loading-exploring-wikipedia-data.en.srt
04_computing-exploring-tf-idfs.en.txt
07_examples-of-document-retrieval-in-action.en.srt
03_exploring-word-counts.en.txt
06_building-exploring-a-nearest-neighbors-model-for-wikipedia-articles.en.srt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html
06_building-exploring-a-nearest-neighbors-model-for-wikipedia-articles.en.txt
07_examples-of-document-retrieval-in-action.en.txt
05_computing-distances-between-wikipedia-articles.en.txt
02_loading-exploring-wikipedia-data.en.txt
03_exploring-word-counts.mp4
04_computing-exploring-tf-idfs.mp4
02_loading-exploring-wikipedia-data.mp4
05_computing-distances-between-wikipedia-articles.mp4
07_examples-of-document-retrieval-in-action.mp4
06_building-exploring-a-nearest-neighbors-model-for-wikipedia-articles.mp4
01_algorithms-for-retrieval-and-measuring-similarity-of-documents
05_prioritizing-important-words-with-tf-idf.en.srt
04_word-count-representation-for-measuring-similarity.en.srt
04_word-count-representation-for-measuring-similarity.en.txt
06_calculating-tf-idf-vectors.en.srt
03_what-is-the-document-retrieval-task.en.srt
01_slides-presented-in-this-module_instructions.html
02_document-retrieval-a-case-study-in-clustering-and-measuring-similarity.e
03_what-is-the-document-retrieval-task.en.txt
07_retrieving-similar-documents-using-nearest-neighbor-search.en.txt
06_calculating-tf-idf-vectors.en.txt
07_retrieving-similar-documents-using-nearest-neighbor-search.en.srt
05_prioritizing-important-words-with-tf-idf.en.txt
04_word-count-representation-for-measuring-similarity.mp4
06_calculating-tf-idf-vectors.mp4
01_slides-presented-in-this-module_clustering-intro-annotated.pdf
05_prioritizing-important-words-with-tf-idf.mp4
07_retrieving-similar-documents-using-nearest-neighbor-search.mp4
03_what-is-the-document-retrieval-task.mp4
02_document-retrieval-a-case-study-in-clustering-and-measuring-similarity.m
02_clustering-models-and-algorithms
04_other-examples-of-clustering.en.srt
02_clustering-documents-an-unsupervised-learning-task.en.srt
03_k-means-a-clustering-algorithm.en.srt
04_other-examples-of-clustering.en.txt
01_clustering-documents-task-overview.en.txt
02_clustering-documents-an-unsupervised-learning-task.en.txt
01_clustering-documents-task-overview.en.srt
03_k-means-a-clustering-algorithm.en.txt
04_other-examples-of-clustering.mp4
02_clustering-documents-an-unsupervised-learning-task.mp4
01_clustering-documents-task-overview.mp4
03_k-means-a-clustering-algorithm.mp4
02_regression-predicting-house-prices
04_predicting-house-prices-jupyter-notebook
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01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND02-NB01.ipynb.zip
11_applying-learned-models-to-predict-price-of-two-fancy-houses.en.srt
02_loading-exploring-house-sale-data.en.srt
08_exploring-other-features-of-the-data.en.srt
10_applying-learned-models-to-predict-price-of-an-average-house.en.srt
06_visualizing-predictions-of-simple-model-with-matplotlib.en.srt
02_loading-exploring-house-sale-data.en.txt
11_applying-learned-models-to-predict-price-of-two-fancy-houses.en.txt
08_exploring-other-features-of-the-data.en.txt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html
03_splitting-the-data-into-training-and-test-sets.en.srt
03_splitting-the-data-into-training-and-test-sets.en.txt
04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.en.srt
04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.en.txt
05_evaluating-error-rmse-of-the-simple-model.en.srt
05_evaluating-error-rmse-of-the-simple-model.en.txt
07_inspecting-the-model-coefficients-learned.en.srt
07_inspecting-the-model-coefficients-learned.en.txt
09_learning-a-model-to-predict-house-prices-from-more-features.en.txt
09_learning-a-model-to-predict-house-prices-from-more-features.en.srt
10_applying-learned-models-to-predict-price-of-an-average-house.en.txt
06_visualizing-predictions-of-simple-model-with-matplotlib.en.txt
11_applying-learned-models-to-predict-price-of-two-fancy-houses.mp4
02_loading-exploring-house-sale-data.mp4
08_exploring-other-features-of-the-data.mp4
10_applying-learned-models-to-predict-price-of-an-average-house.mp4
06_visualizing-predictions-of-simple-model-with-matplotlib.mp4
04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.mp4
09_learning-a-model-to-predict-house-prices-from-more-features.mp4
03_splitting-the-data-into-training-and-test-sets.mp4
05_evaluating-error-rmse-of-the-simple-model.mp4
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_house_images.zip
07_inspecting-the-model-coefficients-learned.mp4
05_programming-assignment
01_predicting-house-prices-assignment_home_data.sframe.zip
01_predicting-house-prices-assignment_graphlab.SFrame.html
01_predicting-house-prices-assignment_FND02-NB01.ipynb.zip
01_predicting-house-prices-assignment_instructions.html
02_predicting-house-prices_exam.html
01_predicting-house-prices-assignment_house_images.zip
01_predicting-house-prices-assignment_home_data.csv
01_linear-regression-modeling
04_linear-regression-a-model-based-approach.en.txt
04_linear-regression-a-model-based-approach.en.srt
05_adding-higher-order-effects.en.srt
03_what-is-the-goal-and-how-might-you-naively-address-it.en.srt
01_slides-presented-in-this-module_instructions.html
02_predicting-house-prices-a-case-study-in-regression.en.srt
02_predicting-house-prices-a-case-study-in-regression.en.txt
05_adding-higher-order-effects.en.txt
03_what-is-the-goal-and-how-might-you-naively-address-it.en.txt
01_slides-presented-in-this-module_regression-intro-annotated.pdf
04_linear-regression-a-model-based-approach.mp4
05_adding-higher-order-effects.mp4
03_what-is-the-goal-and-how-might-you-naively-address-it.mp4
02_predicting-house-prices-a-case-study-in-regression.mp4
03_summary-of-regression
02_regression_exam.html
01_regression-ml-block-diagram.en.srt
01_regression-ml-block-diagram.en.txt
01_regression-ml-block-diagram.mp4
02_evaluating-regression-models
01_evaluating-overfitting-via-training-test-split.en.srt
02_training-test-curves.en.srt
01_evaluating-overfitting-via-training-test-split.en.txt
04_other-regression-examples.en.srt
03_adding-other-features.en.txt
03_adding-other-features.en.srt
02_training-test-curves.en.txt
04_other-regression-examples.en.txt
01_evaluating-overfitting-via-training-test-split.mp4
04_other-regression-examples.mp4
02_training-test-curves.mp4
03_adding-other-features.mp4
03_classification-analyzing-sentiment
05_programming-assignment
01_analyzing-product-sentiment-assignment_amazon_baby.csv
01_analyzing-product-sentiment-assignment_amazon_baby.sframe.zip
01_analyzing-product-sentiment-assignment_datastructures.html
01_analyzing-product-sentiment-assignment_instructions.html
01_analyzing-product-sentiment-assignment_FND03-NB01.ipynb.zip
01_analyzing-product-sentiment-assignment_turicreate.SArray.apply.html
02_analyzing-product-sentiment_exam.html
01_classification-modeling
02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.en.srt
04_examples-of-classification-tasks.en.srt
05_linear-classifiers.en.srt
03_what-is-an-intelligent-restaurant-review-system.en.srt
06_decision-boundaries.en.srt
04_examples-of-classification-tasks.en.txt
05_linear-classifiers.en.txt
01_slides-presented-in-this-module_instructions.html
02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.en.txt
03_what-is-an-intelligent-restaurant-review-system.en.txt
06_decision-boundaries.en.txt
04_examples-of-classification-tasks.mp4
05_linear-classifiers.mp4
03_what-is-an-intelligent-restaurant-review-system.mp4
06_decision-boundaries.mp4
01_slides-presented-in-this-module_classification-annotated.pdf
02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.mp4
04_analyzing-sentiment-jupyter-notebook
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_amazon_baby.sframe.zip
03_creating-the-word-count-vector.en.txt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND03-NB01.ipynb.zip
09_exploring-the-most-positive-negative-aspects-of-a-product.en.srt
07_evaluating-a-classifier-the-roc-curve.en.srt
04_exploring-the-most-popular-product.en.srt
08_applying-model-to-find-most-positive-negative-reviews-for-a-product.en.srt
05_defining-which-reviews-have-positive-or-negative-sentiment.en.srt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html
02_loading-exploring-product-review-data.en.txt
03_creating-the-word-count-vector.en.srt
05_defining-which-reviews-have-positive-or-negative-sentiment.en.txt
06_training-a-sentiment-classifier.en.txt
06_training-a-sentiment-classifier.en.srt
09_exploring-the-most-positive-negative-aspects-of-a-product.en.txt
07_evaluating-a-classifier-the-roc-curve.en.txt
04_exploring-the-most-popular-product.en.txt
02_loading-exploring-product-review-data.en.srt
08_applying-model-to-find-most-positive-negative-reviews-for-a-product.en.txt
09_exploring-the-most-positive-negative-aspects-of-a-product.mp4
08_applying-model-to-find-most-positive-negative-reviews-for-a-product.mp4
04_exploring-the-most-popular-product.mp4
07_evaluating-a-classifier-the-roc-curve.mp4
05_defining-which-reviews-have-positive-or-negative-sentiment.mp4
06_training-a-sentiment-classifier.mp4
02_loading-exploring-product-review-data.mp4
03_creating-the-word-count-vector.mp4
03_summary-of-classification
02_classification_exam.html
01_classification-ml-block-diagram.en.srt
01_classification-ml-block-diagram.en.txt
01_classification-ml-block-diagram.mp4
02_evaluating-classification-models
03_false-positives-false-negatives-and-confusion-matrices.en.srt
04_learning-curves.en.srt
01_training-and-evaluating-a-classifier.en.srt
03_false-positives-false-negatives-and-confusion-matrices.en.txt
04_learning-curves.en.txt
02_whats-a-good-accuracy.en.srt
02_whats-a-good-accuracy.en.txt
05_class-probabilities.en.srt
05_class-probabilities.en.txt
01_training-and-evaluating-a-classifier.en.txt
04_learning-curves.mp4
03_false-positives-false-negatives-and-confusion-matrices.mp4
02_whats-a-good-accuracy.mp4
01_training-and-evaluating-a-classifier.mp4
05_class-probabilities.mp4
01_welcome
03_getting-started-with-the-tools-for-the-course
01_getting-started-with-python-jupyter-notebook-turi-create_Turi_Getting_Started_with_SFrames.ipynb.zip
01_getting-started-with-python-jupyter-notebook-turi-create_people-example.csv
02_where-should-my-files-go_instructions.html
01_getting-started-with-python-jupyter-notebook-turi-create_instructions.html
01_getting-started-with-python-jupyter-notebook-turi-create_Getting_started_with_Jupyter_Notebook.ipynb.zip
03_important-changes-from-previous-courses_instructions.html
06_more-sframes-practice
01_download-wiki-people-data_people_wiki.sframe.zip
01_download-wiki-people-data_instructions.html
02_sframes_exam.html
04_getting-started-with-python-and-the-jupyter-notebook
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html
04_conditional-statements-and-loops-in-python.en.srt
03_creating-variables-in-python.en.srt
02_starting-a-jupyter-notebook.en.srt
04_conditional-statements-and-loops-in-python.en.txt
03_creating-variables-in-python.en.txt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_Getting_started_with_Jupyter_Notebook.ipynb.zip
05_creating-functions-and-lambdas-in-python.en.txt
02_starting-a-jupyter-notebook.en.txt
05_creating-functions-and-lambdas-in-python.en.srt
04_conditional-statements-and-loops-in-python.mp4
03_creating-variables-in-python.mp4
02_starting-a-jupyter-notebook.mp4
05_creating-functions-and-lambdas-in-python.mp4
02_who-this-specialization-is-for-and-what-you-will-be-able-to-do
03_what-you-ll-be-able-to-do.en.txt
04_the-capstone-and-an-example-intelligent-application.en.srt
04_the-capstone-and-an-example-intelligent-application.en.txt
02_who-is-this-specialization-for.en.srt
01_how-we-got-into-ml.en.srt
03_what-you-ll-be-able-to-do.en.srt
05_the-future-of-intelligent-applications.en.txt
05_the-future-of-intelligent-applications.en.srt
02_who-is-this-specialization-for.en.txt
01_how-we-got-into-ml.en.txt
04_the-capstone-and-an-example-intelligent-application.mp4
01_how-we-got-into-ml.mp4
02_who-is-this-specialization-for.mp4
05_the-future-of-intelligent-applications.mp4
03_what-you-ll-be-able-to-do.mp4
01_why-you-should-learn-machine-learning-with-us
03_welcome-to-this-course-and-specialization.en.txt
06_why-a-case-study-approach.en.srt
07_specialization-overview.en.srt
04_who-we-are.en.srt
06_why-a-case-study-approach.en.txt
07_specialization-overview.en.txt
05_machine-learning-is-changing-the-world.en.srt
04_who-we-are.en.txt
01_important-update-regarding-the-machine-learning-specialization_instructions.html
02_slides-presented-in-this-module_instructions.html
03_welcome-to-this-course-and-specialization.en.srt
05_machine-learning-is-changing-the-world.en.txt
06_why-a-case-study-approach.mp4
04_who-we-are.mp4
07_specialization-overview.mp4
05_machine-learning-is-changing-the-world.mp4
02_slides-presented-in-this-module_intro.pdf
03_welcome-to-this-course-and-specialization.mp4
05_getting-started-with-sframes-for-data-engineering-and-analysis
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_people-example.csv
05_using-apply-for-data-transformation.en.srt
02_starting-turi-create-loading-an-sframe.en.srt
04_interacting-with-columns-of-an-sframe.en.srt
03_canvas-for-data-visualization.en.srt
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html
01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_Turi_Getting_Started_with_SFrames.ipynb.zip
04_interacting-with-columns-of-an-sframe.en.txt
05_using-apply-for-data-transformation.en.txt
02_starting-turi-create-loading-an-sframe.en.txt
03_canvas-for-data-visualization.en.txt
05_using-apply-for-data-transformation.mp4
02_starting-turi-create-loading-an-sframe.mp4
04_interacting-with-columns-of-an-sframe.mp4
03_canvas-for-data-visualization.mp4
07_closing-remarks
01_deploying-machine-learning-as-a-service
02_you-ve-made-it.en.srt
04_what-happens-after-deployment.en.srt
04_what-happens-after-deployment.en.txt
03_deploying-an-ml-service.en.srt
03_deploying-an-ml-service.en.txt
01_slides-presented-in-this-module_instructions.html
02_you-ve-made-it.en.txt
04_what-happens-after-deployment.mp4
01_slides-presented-in-this-module_closing.pdf
03_deploying-an-ml-service.mp4
02_you-ve-made-it.mp4
02_machine-learning-challenges-and-future-directions
02_where-is-ml-going.mp4
01_open-challenges-in-ml.en.srt
02_where-is-ml-going.en.srt
01_open-challenges-in-ml.mp4
03_whats-ahead-in-the-specialization.en.srt
01_open-challenges-in-ml.en.txt
02_where-is-ml-going.en.txt
03_whats-ahead-in-the-specialization.en.txt
04_thank-you.en.srt
04_thank-you.en.txt
03_whats-ahead-in-the-specialization.mp4
04_thank-you.mp4
08_Resources
01_discussion-forums-and-mentors
01__MentorProgramInformation.pdf
01__resources.html
ml-regression
07_nearest-neighbors-kernel-regression
06_programming-assignment
01_predicting-house-prices-using-k-nearest-neighbors-regression_numpy.argsort.html
01_predicting-house-prices-using-k-nearest-neighbors-regression_home_data_small.sframe.zip
01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_train.csv.zip
01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small.csv.zip
01_predicting-house-prices-using-k-nearest-neighbors-regression_instructions.html
01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_test.csv.zip
01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_validation.csv.zip
01_predicting-house-prices-using-k-nearest-neighbors-regression_numpy.ndarray.shape.html
01_predicting-house-prices-using-k-nearest-neighbors-regression_REG06-NB01.ipynb.zip
01_predicting-house-prices-using-k-nearest-neighbors-regression_numpy-tutorial-py3.ipynb.zip
03_k-nearest-neighbors-and-weighted-k-nearest-neighbors
02_k-nearest-neighbors-in-practice.en.txt
01_k-nearest-neighbors-regression.en.srt
01_k-nearest-neighbors-regression.en.txt
03_weighted-k-nearest-neighbors.en.srt
02_k-nearest-neighbors-in-practice.en.srt
03_weighted-k-nearest-neighbors.en.txt
01_k-nearest-neighbors-regression.mp4
03_weighted-k-nearest-neighbors.mp4
02_k-nearest-neighbors-in-practice.mp4
05_k-nn-and-kernel-regression-wrapup
01_performance-of-nn-as-amount-of-data-grows.en.srt
01_performance-of-nn-as-amount-of-data-grows.en.txt
02_issues-with-high-dimensions-data-scarcity-and-computational-complexity.en.srt
02_issues-with-high-dimensions-data-scarcity-and-computational-complexity.en.txt
03_k-nn-for-classification.en.srt
04_a-brief-recap.en.srt
03_k-nn-for-classification.en.txt
04_a-brief-recap.en.txt
01_performance-of-nn-as-amount-of-data-grows.mp4
02_issues-with-high-dimensions-data-scarcity-and-computational-complexity.mp4
03_k-nn-for-classification.mp4
04_a-brief-recap.mp4
02_nearest-neighbor-regression
01_1-nearest-neighbor-regression-approach.en.srt
01_1-nearest-neighbor-regression-approach.en.txt
02_distance-metrics.en.srt
03_1-nearest-neighbor-algorithm.en.srt
02_distance-metrics.en.txt
03_1-nearest-neighbor-algorithm.en.txt
01_1-nearest-neighbor-regression-approach.mp4
02_distance-metrics.mp4
03_1-nearest-neighbor-algorithm.mp4
04_kernel-regression
01_from-weighted-k-nn-to-kernel-regression.en.srt
02_global-fits-of-parametric-models-vs-local-fits-of-kernel-regression.en.srt
01_from-weighted-k-nn-to-kernel-regression.en.txt
02_global-fits-of-parametric-models-vs-local-fits-of-kernel-regression.en.txt
02_global-fits-of-parametric-models-vs-local-fits-of-kernel-regression.mp4
01_from-weighted-k-nn-to-kernel-regression.mp4
01_motivating-local-fits
02_limitations-of-parametric-regression.en.srt
02_limitations-of-parametric-regression.en.txt
01_slides-presented-in-this-module_instructions.html
02_limitations-of-parametric-regression.mp4
01_slides-presented-in-this-module_week6_NNkernelregression-annotated.pdf
04_assessing-performance
01_defining-how-we-assess-performance
02_assessing-performance-intro.en.txt
02_assessing-performance-intro.mp4
02_assessing-performance-intro.en.srt
03_what-do-we-mean-by-loss.en.srt
03_what-do-we-mean-by-loss.en.txt
01_slides-presented-in-this-module_instructions.html
03_what-do-we-mean-by-loss.mp4
01_slides-presented-in-this-module_week3_assessingperformance-annotated.pdf
04_optional-advanced-material-formally-defining-and-deriving-the-3-sources-of-error
02_formally-deriving-why-3-sources-of-error.en.srt
02_formally-deriving-why-3-sources-of-error.mp4
01_formally-defining-the-3-sources-of-error.mp4
01_formally-defining-the-3-sources-of-error.en.srt
02_formally-deriving-why-3-sources-of-error.en.txt
01_formally-defining-the-3-sources-of-error.en.txt
06_programming-assignment
01_polynomial-regression_wk3_kc_house_train_data.csv.zip
01_polynomial-regression_wk3_kc_house_test_data.csv.zip
01_polynomial-regression_wk3_kc_house_valid_data.csv.zip
01_polynomial-regression_home_data.sframe.zip
01_polynomial-regression_kc_house_data.csv.zip
01_polynomial-regression_wk3_kc_house_set_3_data.csv.zip
01_polynomial-regression_wk3_kc_house_set_1_data.csv.zip
01_polynomial-regression_wk3_kc_house_set_4_data.csv.zip
01_polynomial-regression_wk3_kc_house_set_2_data.csv.zip
01_polynomial-regression_instructions.html
01_polynomial-regression_REG03-NB01.ipynb.zip
01_polynomial-regression_numpy-tutorial-py3.ipynb.zip
02_3-measures-of-loss-and-their-trends-with-model-complexity
03_test-error-what-we-can-actually-compute.en.srt
02_generalization-error-what-we-really-want.en.srt
01_training-error-assessing-loss-on-the-training-set.en.srt
02_generalization-error-what-we-really-want.en.txt
01_training-error-assessing-loss-on-the-training-set.en.txt
03_test-error-what-we-can-actually-compute.en.txt
04_defining-overfitting.en.srt
05_training-test-split.en.srt
05_training-test-split.en.txt
04_defining-overfitting.en.txt
02_generalization-error-what-we-really-want.mp4
01_training-error-assessing-loss-on-the-training-set.mp4
03_test-error-what-we-can-actually-compute.mp4
04_defining-overfitting.mp4
05_training-test-split.mp4
05_putting-the-pieces-together
01_training-validation-test-split-for-model-selection-fitting-and-assessment.en.srt
01_training-validation-test-split-for-model-selection-fitting-and-assessment.en.txt
02_a-brief-recap.en.srt
02_a-brief-recap.en.txt
01_training-validation-test-split-for-model-selection-fitting-and-assessment.mp4
02_a-brief-recap.mp4
03_3-sources-of-error-and-the-bias-variance-tradeoff
01_irreducible-error-and-bias.en.srt
02_variance-and-the-bias-variance-tradeoff.en.srt
03_error-vs-amount-of-data.en.srt
01_irreducible-error-and-bias.en.txt
02_variance-and-the-bias-variance-tradeoff.en.txt
03_error-vs-amount-of-data.en.txt
01_irreducible-error-and-bias.mp4
02_variance-and-the-bias-variance-tradeoff.mp4
03_error-vs-amount-of-data.mp4
02_simple-linear-regression
06_discussion-and-summary-of-simple-linear-regression
06_a-brief-recap.en.srt
01_download-notebooks-to-follow-along_PhillyCrime.ipynb.zip
03_influence-of-high-leverage-points-removing-center-city.en.srt
03_influence-of-high-leverage-points-removing-center-city.en.txt
02_influence-of-high-leverage-points-exploring-the-data.en.srt
05_asymmetric-cost-functions.en.srt
04_influence-of-high-leverage-points-removing-high-end-towns.en.srt
02_influence-of-high-leverage-points-exploring-the-data.en.txt
05_asymmetric-cost-functions.en.txt
04_influence-of-high-leverage-points-removing-high-end-towns.en.txt
01_download-notebooks-to-follow-along_Philadelphia_Crime_Rate_noNA.csv.zip
01_download-notebooks-to-follow-along_instructions.html
06_a-brief-recap.en.txt
03_influence-of-high-leverage-points-removing-center-city.mp4
02_influence-of-high-leverage-points-exploring-the-data.mp4
05_asymmetric-cost-functions.mp4
04_influence-of-high-leverage-points-removing-high-end-towns.mp4
06_a-brief-recap.mp4
07_programming-assignment
01_fitting-a-simple-linear-regression-model-on-housing-data_home_data.sframe.zip
01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_data.csv.zip
01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_train_data.csv.zip
01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_test_data.csv.zip
01_fitting-a-simple-linear-regression-model-on-housing-data_instructions.html
01_fitting-a-simple-linear-regression-model-on-housing-data_REG01-NB01.ipynb.zip
02_the-simple-linear-regression-model-its-use-and-interpretation
03_using-the-fitted-line.en.txt
02_the-cost-of-using-a-given-line.en.srt
03_using-the-fitted-line.en.srt
04_interpreting-the-fitted-line.en.srt
02_the-cost-of-using-a-given-line.en.txt
04_interpreting-the-fitted-line.en.txt
01_the-simple-linear-regression-model.en.srt
01_the-simple-linear-regression-model.en.txt
02_the-cost-of-using-a-given-line.mp4
03_using-the-fitted-line.mp4
04_interpreting-the-fitted-line.mp4
01_the-simple-linear-regression-model.mp4
01_regression-fundamentals
02_a-case-study-in-predicting-house-prices.en.txt
03_regression-fundamentals-data-model.en.srt
03_regression-fundamentals-data-model.en.txt
05_regression-ml-block-diagram.en.srt
05_regression-ml-block-diagram.en.txt
04_regression-fundamentals-the-task.en.srt
04_regression-fundamentals-the-task.en.txt
02_a-case-study-in-predicting-house-prices.en.srt
01_slides-presented-in-this-module_instructions.html
03_regression-fundamentals-data-model.mp4
05_regression-ml-block-diagram.mp4
01_slides-presented-in-this-module_week1_simpleregression-annotated.pdf
04_regression-fundamentals-the-task.mp4
02_a-case-study-in-predicting-house-prices.mp4
05_finding-the-least-squares-line
05_optional-reading-worked-out-example-for-gradient-descent_instructions.html
03_optional-reading-worked-out-example-for-closed-form-solution_instructions.html
01_computing-the-gradient-of-rss.en.srt
04_approach-2-gradient-descent.en.srt
02_approach-1-closed-form-solution.en.srt
01_computing-the-gradient-of-rss.en.txt
04_approach-2-gradient-descent.en.txt
02_approach-1-closed-form-solution.en.txt
06_comparing-the-approaches.en.srt
06_comparing-the-approaches.en.txt
04_approach-2-gradient-descent.mp4
01_computing-the-gradient-of-rss.mp4
02_approach-1-closed-form-solution.mp4
06_comparing-the-approaches.mp4
03_an-aside-on-optimization-one-dimensional-objectives
04_finding-the-max-via-hill-climbing.en.srt
06_choosing-stepsize-and-convergence-criteria.en.srt
02_finding-maxima-or-minima-analytically.en.srt
06_choosing-stepsize-and-convergence-criteria.en.txt
04_finding-the-max-via-hill-climbing.en.txt
02_finding-maxima-or-minima-analytically.en.txt
01_defining-our-least-squares-optimization-objective.en.srt
05_finding-the-min-via-hill-descent.en.srt
03_maximizing-a-1d-function-a-worked-example.en.srt
01_defining-our-least-squares-optimization-objective.en.txt
05_finding-the-min-via-hill-descent.en.txt
03_maximizing-a-1d-function-a-worked-example.en.txt
02_finding-maxima-or-minima-analytically.mp4
04_finding-the-max-via-hill-climbing.mp4
06_choosing-stepsize-and-convergence-criteria.mp4
01_defining-our-least-squares-optimization-objective.mp4
05_finding-the-min-via-hill-descent.mp4
03_maximizing-a-1d-function-a-worked-example.mp4
04_an-aside-on-optimization-multidimensional-objectives
01_gradients-derivatives-in-multiple-dimensions.en.srt
02_gradient-descent-multidimensional-hill-descent.en.srt
01_gradients-derivatives-in-multiple-dimensions.en.txt
02_gradient-descent-multidimensional-hill-descent.en.txt
02_gradient-descent-multidimensional-hill-descent.mp4
01_gradients-derivatives-in-multiple-dimensions.mp4
05_ridge-regression
01_characteristics-of-overfit-models
03_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip
04_overfitting-demo.en.srt
04_overfitting-demo.en.txt
05_overfitting-for-more-general-multiple-regression-models.en.srt
05_overfitting-for-more-general-multiple-regression-models.en.txt
02_symptoms-of-overfitting-in-polynomial-regression.en.srt
02_symptoms-of-overfitting-in-polynomial-regression.en.txt
03_download-the-notebook-and-follow-along_instructions.html
01_slides-presented-in-this-module_instructions.html
04_overfitting-demo.mp4
05_overfitting-for-more-general-multiple-regression-models.mp4
02_symptoms-of-overfitting-in-polynomial-regression.mp4
01_slides-presented-in-this-module_week4_ridgeregression-annotated.pdf
05_programming-assignment-1
01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_train_valid_shuffled.csv.zip
01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_train_data.csv.zip
01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_test_data.csv.zip
01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_valid_data.csv.zip
01_observing-effects-of-l2-penalty-in-polynomial-regression_home_data.sframe.zip
01_observing-effects-of-l2-penalty-in-polynomial-regression_kc_house_data.csv.zip
01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_3_data.csv.zip
01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_1_data.csv.zip
01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_4_data.csv.zip
01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_2_data.csv.zip
01_observing-effects-of-l2-penalty-in-polynomial-regression_instructions.html
01_observing-effects-of-l2-penalty-in-polynomial-regression_REG04-NB01.ipynb.zip
01_observing-effects-of-l2-penalty-in-polynomial-regression_numpy-tutorial-py3.ipynb.zip
04_tying-up-the-loose-ends
04_a-brief-recap.en.txt
03_how-to-handle-the-intercept.en.srt
02_k-fold-cross-validation.en.srt
01_selecting-tuning-parameters-via-cross-validation.en.srt
03_how-to-handle-the-intercept.en.txt
02_k-fold-cross-validation.en.txt
01_selecting-tuning-parameters-via-cross-validation.en.txt
04_a-brief-recap.en.srt
03_how-to-handle-the-intercept.mp4
02_k-fold-cross-validation.mp4
01_selecting-tuning-parameters-via-cross-validation.mp4
04_a-brief-recap.mp4
06_programming-assignment-2
01_implementing-ridge-regression-via-gradient-descent_home_data.sframe.zip
01_implementing-ridge-regression-via-gradient-descent_kc_house_data.csv.zip
01_implementing-ridge-regression-via-gradient-descent_kc_house_train_data.csv.zip
01_implementing-ridge-regression-via-gradient-descent_kc_house_test_data.csv.zip
01_implementing-ridge-regression-via-gradient-descent_instructions.html
01_implementing-ridge-regression-via-gradient-descent_REG04-NB02.ipynb.zip
01_implementing-ridge-regression-via-gradient-descent_numpy-tutorial-py3.ipynb.zip
02_the-ridge-objective
04_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip
05_ridge-regression-demo.en.srt
01_balancing-fit-and-magnitude-of-coefficients.en.srt
05_ridge-regression-demo.en.txt
02_the-resulting-ridge-objective-and-its-extreme-solutions.en.srt
01_balancing-fit-and-magnitude-of-coefficients.en.txt
06_the-ridge-coefficient-path.en.srt
02_the-resulting-ridge-objective-and-its-extreme-solutions.en.txt
06_the-ridge-coefficient-path.en.txt
03_how-ridge-regression-balances-bias-and-variance.en.srt
04_download-the-notebook-and-follow-along_instructions.html
03_how-ridge-regression-balances-bias-and-variance.en.txt
05_ridge-regression-demo.mp4
01_balancing-fit-and-magnitude-of-coefficients.mp4
02_the-resulting-ridge-objective-and-its-extreme-solutions.mp4
06_the-ridge-coefficient-path.mp4
03_how-ridge-regression-balances-bias-and-variance.mp4
03_optimizing-the-ridge-objective
04_approach-2-gradient-descent.en.srt
02_approach-1-closed-form-solution.en.srt
03_discussing-the-closed-form-solution.en.srt
04_approach-2-gradient-descent.en.txt
01_computing-the-gradient-of-the-ridge-objective.en.srt
02_approach-1-closed-form-solution.en.txt
03_discussing-the-closed-form-solution.en.txt
01_computing-the-gradient-of-the-ridge-objective.en.txt
04_approach-2-gradient-descent.mp4
01_computing-the-gradient-of-the-ridge-objective.mp4
03_discussing-the-closed-form-solution.mp4
02_approach-1-closed-form-solution.mp4
03_multiple-regression
01_multiple-features-of-one-input
02_multiple-regression-intro.mp4
04_modeling-seasonality.en.srt
02_multiple-regression-intro.en.srt
04_modeling-seasonality.en.txt
03_polynomial-regression.en.srt
05_where-we-see-seasonality.en.srt
03_polynomial-regression.en.txt
06_regression-with-general-features-of-1-input.en.srt
05_where-we-see-seasonality.en.txt
06_regression-with-general-features-of-1-input.en.txt
01_slides-presented-in-this-module_instructions.html
02_multiple-regression-intro.en.txt
04_modeling-seasonality.mp4
03_polynomial-regression.mp4
05_where-we-see-seasonality.mp4
01_slides-presented-in-this-module_week2_multipleregression-annotated.pdf
06_regression-with-general-features-of-1-input.mp4
06_programming-assignment-1
01_exploring-different-multiple-regression-models-for-house-price-prediction_home_data.sframe.zip
01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_data.csv.zip
01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_train_data.csv.zip
01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_test_data.csv.zip
01_exploring-different-multiple-regression-models-for-house-price-prediction_instructions.html
01_exploring-different-multiple-regression-models-for-house-price-prediction_REG02-NB01.ipynb.zip
07_programming-assignment-2
02_implementing-gradient-descent-for-multiple-regression_home_data.sframe.zip
02_implementing-gradient-descent-for-multiple-regression_kc_house_data.csv.zip
02_implementing-gradient-descent-for-multiple-regression_kc_house_train_data.csv.zip
01_numpy-tutorial_quickstart.html
02_implementing-gradient-descent-for-multiple-regression_kc_house_test_data.csv.zip
02_implementing-gradient-descent-for-multiple-regression_instructions.html
02_implementing-gradient-descent-for-multiple-regression_REG02-NB02.ipynb.zip
01_numpy-tutorial_numpy-tutorial.ipynb.zip
02_implementing-gradient-descent-for-multiple-regression_numpy-tutorial-py3.ipynb.zip.ipynb
01_numpy-tutorial_instructions.html
04_computing-the-least-squares-d-dimensional-curve
01_computing-the-gradient-of-rss.en.srt
05_feature-by-feature-update.en.srt
03_discussing-the-closed-form-solution.en.srt
05_feature-by-feature-update.en.txt
06_algorithmic-summary-of-gradient-descent-approach.en.srt
02_approach-1-closed-form-solution.en.srt
03_discussing-the-closed-form-solution.en.txt
06_algorithmic-summary-of-gradient-descent-approach.en.txt
02_approach-1-closed-form-solution.en.txt
01_computing-the-gradient-of-rss.en.txt
04_approach-2-gradient-descent.en.srt
04_approach-2-gradient-descent.en.txt
05_feature-by-feature-update.mp4
06_algorithmic-summary-of-gradient-descent-approach.mp4
03_discussing-the-closed-form-solution.mp4
02_approach-1-closed-form-solution.mp4
01_computing-the-gradient-of-rss.mp4
04_approach-2-gradient-descent.mp4
03_setting-the-stage-for-computing-the-least-squares-fit
04_computing-the-cost-of-a-d-dimensional-curve.en.srt
02_rewriting-the-single-observation-model-in-vector-notation.en.srt
04_computing-the-cost-of-a-d-dimensional-curve.en.txt
03_rewriting-the-model-for-all-observations-in-matrix-notation.en.srt
02_rewriting-the-single-observation-model-in-vector-notation.en.txt
03_rewriting-the-model-for-all-observations-in-matrix-notation.en.txt
01_optional-reading-review-of-matrix-algebra_instructions.html
04_computing-the-cost-of-a-d-dimensional-curve.mp4
02_rewriting-the-single-observation-model-in-vector-notation.mp4
03_rewriting-the-model-for-all-observations-in-matrix-notation.mp4
02_incorporating-multiple-inputs
04_interpreting-the-multiple-regression-fit.en.srt
04_interpreting-the-multiple-regression-fit.en.txt
01_motivating-the-use-of-multiple-inputs.en.srt
03_regression-with-features-of-multiple-inputs.en.srt
02_defining-notation.en.srt
01_motivating-the-use-of-multiple-inputs.en.txt
03_regression-with-features-of-multiple-inputs.en.txt
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01_motivating-the-use-of-multiple-inputs.mp4
03_regression-with-features-of-multiple-inputs.mp4
02_defining-notation.mp4
05_summarizing-multiple-regression
01_a-brief-recap.en.srt
01_a-brief-recap.en.txt
01_a-brief-recap.mp4
06_feature-selection-lasso
08_programming-assignment-1
01_using-lasso-to-select-features_wk3_kc_house_train_data.csv.zip
01_using-lasso-to-select-features_wk3_kc_house_test_data.csv.zip
01_using-lasso-to-select-features_home_data.sframe.zip
01_using-lasso-to-select-features_wk3_kc_house_valid_data.csv.zip
01_using-lasso-to-select-features_kc_house_data.csv.zip
01_using-lasso-to-select-features_instructions.html
01_using-lasso-to-select-features_REG05-NB01.ipynb.zip
01_using-lasso-to-select-features_numpy-tutorial-py3.ipynb.zip
09_programming-assignment-2
01_implementing-lasso-using-coordinate-descent_home_data.sframe.zip
01_implementing-lasso-using-coordinate-descent_kc_house_data.csv.zip
01_implementing-lasso-using-coordinate-descent_kc_house_train_data.csv.zip
01_implementing-lasso-using-coordinate-descent_kc_house_test_data.csv.zip
01_implementing-lasso-using-coordinate-descent_instructions.html
01_implementing-lasso-using-coordinate-descent_REG05-NB02.ipynb.zip
01_implementing-lasso-using-coordinate-descent_numpy-tutorial-py3.ipynb.zip
03_geometric-intuition-for-sparsity-of-lasso-solutions
04_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip
01_visualizing-the-ridge-cost.en.srt
03_visualizing-the-lasso-cost-and-solution.en.srt
02_visualizing-the-ridge-solution.en.srt
05_lasso-demo.en.srt
01_visualizing-the-ridge-cost.en.txt
03_visualizing-the-lasso-cost-and-solution.en.txt
05_lasso-demo.en.txt
02_visualizing-the-ridge-solution.en.txt
04_download-the-notebook-and-follow-along_instructions.html
03_visualizing-the-lasso-cost-and-solution.mp4
01_visualizing-the-ridge-cost.mp4
02_visualizing-the-ridge-solution.mp4
05_lasso-demo.mp4
06_optional-advanced-material-deriving-the-lasso-coordinate-descent-update
01_deriving-the-lasso-coordinate-descent-update.mp4
01_deriving-the-lasso-coordinate-descent-update.en.srt
01_deriving-the-lasso-coordinate-descent-update.en.txt
01_feature-selection-via-explicit-model-enumeration
05_greedy-algorithms.en.srt
03_all-subsets.en.srt
05_greedy-algorithms.en.txt
02_the-feature-selection-task.en.srt
03_all-subsets.en.txt
04_complexity-of-all-subsets.en.srt
06_complexity-of-the-greedy-forward-stepwise-algorithm.en.srt
02_the-feature-selection-task.en.txt
04_complexity-of-all-subsets.en.txt
06_complexity-of-the-greedy-forward-stepwise-algorithm.en.txt
01_slides-presented-in-this-module_instructions.html
05_greedy-algorithms.mp4
03_all-subsets.mp4
02_the-feature-selection-task.mp4
01_slides-presented-in-this-module_week5_lassoregression-annotated.pdf
06_complexity-of-the-greedy-forward-stepwise-algorithm.mp4
04_complexity-of-all-subsets.mp4
04_setting-the-stage-for-solving-the-lasso
04_coordinate-descent-for-least-squares-regression-normalized-features.en.srt
02_coordinate-descent.en.srt
04_coordinate-descent-for-least-squares-regression-normalized-features.en.txt
03_normalizing-features.en.srt
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01_what-makes-the-lasso-objective-different.en.srt
03_normalizing-features.en.txt
01_what-makes-the-lasso-objective-different.en.txt
04_coordinate-descent-for-least-squares-regression-normalized-features.mp4
02_coordinate-descent.mp4
03_normalizing-features.mp4
01_what-makes-the-lasso-objective-different.mp4
02_feature-selection-implicitly-via-regularized-regression
03_the-lasso-objective-and-its-coefficient-path.en.srt
02_thresholding-ridge-coefficients.en.srt
01_can-we-use-regularization-for-feature-selection.en.srt
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02_thresholding-ridge-coefficients.mp4
01_can-we-use-regularization-for-feature-selection.mp4
07_tying-up-loose-ends
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02_a-brief-recap.en.srt
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02_a-brief-recap.en.txt
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02_a-brief-recap.mp4
05_optimizing-the-lasso-objective
01_coordinate-descent-for-lasso-normalized-features.en.srt
02_assessing-convergence-and-other-lasso-solvers.en.srt
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01_coordinate-descent-for-lasso-normalized-features.mp4
02_assessing-convergence-and-other-lasso-solvers.mp4
03_coordinate-descent-for-lasso-unnormalized-features.mp4
08_closing-remarks
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02_thank-you.en.srt
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04_feature-selection-lasso-and-nearest-neighbor-regression.mp4
01_slides-presented-in-this-module_closing.pdf
01_welcome
01_what-is-this-course-about
08_reading-software-tools-you-ll-need_quickstart.html
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06_outlining-the-second-half-of-the-course.en.srt
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03_welcome.en.srt
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01_important-update-regarding-the-machine-learning-specialization_instructions.html
02_slides-presented-in-this-module_instructions.html
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06_outlining-the-second-half-of-the-course.mp4
05_outlining-the-first-half-of-the-course.mp4
07_assumed-background.mp4
04_what-is-the-course-about.mp4
03_welcome.mp4
unsupervised-learning-recommenders-reinforcement-learning
03_reinforcement-learning
05_conversations-with-andrew-optional
01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.mp4
01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.txt
01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.srt
01_reinforcement-learning-introduction
03_the-return-in-reinforcement-learning.en.srt
01_what-is-reinforcement-learning.en.srt
02_mars-rover-example.en.srt
05_review-of-key-concepts.en.srt
03_the-return-in-reinforcement-learning.en.txt
01_what-is-reinforcement-learning.en.txt
02_mars-rover-example.en.txt
05_review-of-key-concepts.en.txt
04_making-decisions-policies-in-reinforcement-learning.en.srt
04_making-decisions-policies-in-reinforcement-learning.en.txt
01_what-is-reinforcement-learning.mp4
03_the-return-in-reinforcement-learning.mp4
02_mars-rover-example.mp4
05_review-of-key-concepts.mp4
04_making-decisions-policies-in-reinforcement-learning.mp4
03_continuous-state-spaces
03_learning-the-state-value-function.en.srt
06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.srt
05_algorithm-refinement-greedy-policy.en.srt
03_learning-the-state-value-function.en.txt
01_example-of-continuous-state-space-applications.en.srt
06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.txt
02_lunar-lander.en.srt
05_algorithm-refinement-greedy-policy.en.txt
01_example-of-continuous-state-space-applications.en.txt
02_lunar-lander.en.txt
04_algorithm-refinement-improved-neural-network-architecture.en.srt
07_the-state-of-reinforcement-learning.en.srt
07_the-state-of-reinforcement-learning.en.txt
04_algorithm-refinement-improved-neural-network-architecture.en.txt
03_learning-the-state-value-function.mp4
01_example-of-continuous-state-space-applications.mp4
06_algorithm-refinement-mini-batch-and-soft-updates-optional.mp4
05_algorithm-refinement-greedy-policy.mp4
02_lunar-lander.mp4
07_the-state-of-reinforcement-learning.mp4
04_algorithm-refinement-improved-neural-network-architecture.mp4
02_state-action-value-function
03_bellman-equation.en.srt
01_state-action-value-function-definition.en.srt
04_random-stochastic-environment-optional.en.srt
03_bellman-equation.en.txt
01_state-action-value-function-definition.en.txt
04_random-stochastic-environment-optional.en.txt
02_state-action-value-function-example.en.srt
02_state-action-value-function-example.en.txt
03_bellman-equation.mp4
01_state-action-value-function-definition.mp4
04_random-stochastic-environment-optional.mp4
02_state-action-value-function-example.mp4
06_acknowledgments
01_acknowledgments_instructions.html
02_optional-opportunity-to-mentor-other-learners_instructions.html
04_summary-and-thank-you
01_summary-and-thank-you.en.srt
01_summary-and-thank-you.en.txt
01_summary-and-thank-you.mp4
02_recommender-systems
03_content-based-filtering
04_ethical-use-of-recommender-systems.en.txt
04_ethical-use-of-recommender-systems.en.srt
01_collaborative-filtering-vs-content-based-filtering.en.srt
02_deep-learning-for-content-based-filtering.en.srt
03_recommending-from-a-large-catalogue.en.srt
01_collaborative-filtering-vs-content-based-filtering.en.txt
02_deep-learning-for-content-based-filtering.en.txt
05_tensorflow-implementation-of-content-based-filtering.en.srt
03_recommending-from-a-large-catalogue.en.txt
05_tensorflow-implementation-of-content-based-filtering.en.txt
04_ethical-use-of-recommender-systems.mp4
02_deep-learning-for-content-based-filtering.mp4
01_collaborative-filtering-vs-content-based-filtering.mp4
03_recommending-from-a-large-catalogue.mp4
05_tensorflow-implementation-of-content-based-filtering.mp4
01_collaborative-filtering
03_collaborative-filtering-algorithm.en.srt
02_using-per-item-features.en.srt
04_binary-labels-favs-likes-and-clicks.en.srt
03_collaborative-filtering-algorithm.en.txt
02_using-per-item-features.en.txt
01_making-recommendations.en.srt
04_binary-labels-favs-likes-and-clicks.en.txt
01_making-recommendations.en.txt
03_collaborative-filtering-algorithm.mp4
02_using-per-item-features.mp4
01_making-recommendations.mp4
04_binary-labels-favs-likes-and-clicks.mp4
04_principal-component-analysis
01_reducing-the-number-of-features-optional.en.srt
02_pca-algorithm-optional.en.srt
03_pca-in-code-optional.en.srt
02_pca-algorithm-optional.en.txt
01_reducing-the-number-of-features-optional.en.txt
03_pca-in-code-optional.en.txt
02_pca-algorithm-optional.mp4
01_reducing-the-number-of-features-optional.mp4
03_pca-in-code-optional.mp4
02_recommender-systems-implementation-detail
01_mean-normalization.en.srt
02_tensorflow-implementation-of-collaborative-filtering.mp4
02_tensorflow-implementation-of-collaborative-filtering.en.srt
03_finding-related-items.en.srt
02_tensorflow-implementation-of-collaborative-filtering.en.txt
01_mean-normalization.en.txt
03_finding-related-items.en.txt
01_mean-normalization.mp4
03_finding-related-items.mp4
01_unsupervised-learning
03_anomaly-detection
06_choosing-what-features-to-use.en.srt
04_developing-and-evaluating-an-anomaly-detection-system.en.srt
02_gaussian-normal-distribution.en.srt
01_finding-unusual-events.en.srt
03_anomaly-detection-algorithm.en.srt
06_choosing-what-features-to-use.en.txt
05_anomaly-detection-vs-supervised-learning.en.srt
01_finding-unusual-events.en.txt
04_developing-and-evaluating-an-anomaly-detection-system.en.txt
03_anomaly-detection-algorithm.en.txt
02_gaussian-normal-distribution.en.txt
05_anomaly-detection-vs-supervised-learning.en.txt
06_choosing-what-features-to-use.mp4
01_finding-unusual-events.mp4
04_developing-and-evaluating-an-anomaly-detection-system.mp4
02_gaussian-normal-distribution.mp4
03_anomaly-detection-algorithm.mp4
05_anomaly-detection-vs-supervised-learning.mp4
02_clustering
03_k-means-algorithm.en.srt
04_optimization-objective.en.srt
06_choosing-the-number-of-clusters.en.srt
05_initializing-k-means.en.srt
02_k-means-intuition.en.srt
04_optimization-objective.en.txt
03_k-means-algorithm.en.txt
05_initializing-k-means.en.txt
01_what-is-clustering.en.srt
06_choosing-the-number-of-clusters.en.txt
02_k-means-intuition.en.txt
01_what-is-clustering.en.txt
04_optimization-objective.mp4
03_k-means-algorithm.mp4
05_initializing-k-means.mp4
06_choosing-the-number-of-clusters.mp4
02_k-means-intuition.mp4
01_what-is-clustering.mp4
01_welcome-to-the-course
01_welcome.en.srt
01_welcome.en.txt
01_welcome.mp4
ml-clustering-and-retrieval
02_nearest-neighbor-search
04_scaling-up-k-nn-search-using-kd-trees
07_optional-a-worked-out-example-for-kd-trees_instructions.html
02_kd-tree-representation.en.srt
06_approximate-k-nn-search-using-kd-trees.en.srt
03_nn-search-with-kd-trees.en.srt
01_complexity-of-brute-force-search.en.srt
01_complexity-of-brute-force-search.en.txt
04_complexity-of-nn-search-with-kd-trees.en.srt
02_kd-tree-representation.en.txt
06_approximate-k-nn-search-using-kd-trees.en.txt
05_visualizing-scaling-behavior-of-kd-trees.en.srt
03_nn-search-with-kd-trees.en.txt
04_complexity-of-nn-search-with-kd-trees.en.txt
05_visualizing-scaling-behavior-of-kd-trees.en.txt
06_approximate-k-nn-search-using-kd-trees.mp4
02_kd-tree-representation.mp4
03_nn-search-with-kd-trees.mp4
04_complexity-of-nn-search-with-kd-trees.mp4
05_visualizing-scaling-behavior-of-kd-trees.mp4
01_complexity-of-brute-force-search.mp4
06_programming-assignment-2
01_implementing-locality-sensitive-hashing-from-scratch_instructions.html
01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.sframe.zip
01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.gl.zip
01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_tf_idf.npz.zip
01_implementing-locality-sensitive-hashing-from-scratch_itertools.html
01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.csv.zip
01_implementing-locality-sensitive-hashing-from-scratch_sklearn.feature_extraction.text.TfidfVectorizer.html
01_implementing-locality-sensitive-hashing-from-scratch_CLU02-NB02.ipynb.zip
01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_map_index_to_word.json.zip
01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_map_index_to_word.gl.zip
03_programming-assignment-1
01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.sframe.zip
01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.gl.zip
01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_tf_idf.npz.zip
01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.metrics.pairwise.euclidean_distances.html
01_choosing-features-and-metrics-for-nearest-neighbor-search_stdtypes.html
01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.CountVectorizer.html
01_choosing-features-and-metrics-for-nearest-neighbor-search_graphlab.SFrame.join.html
01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.csv.zip
01_choosing-features-and-metrics-for-nearest-neighbor-search_instructions.html
01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.TfidfVectorizer.html
01_choosing-features-and-metrics-for-nearest-neighbor-search_CLU02-NB01.ipynb.zip
01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_word_count.npz.zip
01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_map_index_to_word.json.zip
01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_map_index_to_word.gl.zip
05_locality-sensitive-hashing-for-approximate-nn-search
07_optional-improving-efficiency-through-multiple-tables.mp4
07_optional-improving-efficiency-through-multiple-tables.en.srt
04_defining-more-bins.en.srt
07_optional-improving-efficiency-through-multiple-tables.en.txt
05_searching-neighboring-bins.en.srt
03_using-random-lines-to-partition-points.en.srt
05_searching-neighboring-bins.en.txt
04_defining-more-bins.en.txt
02_lsh-as-an-alternative-to-kd-trees.en.srt
01_limitations-of-kd-trees.en.srt
06_lsh-in-higher-dimensions.en.srt
03_using-random-lines-to-partition-points.en.txt
02_lsh-as-an-alternative-to-kd-trees.en.txt
01_limitations-of-kd-trees.en.txt
06_lsh-in-higher-dimensions.en.txt
05_searching-neighboring-bins.mp4
03_using-random-lines-to-partition-points.mp4
02_lsh-as-an-alternative-to-kd-trees.mp4
01_limitations-of-kd-trees.mp4
04_defining-more-bins.mp4
06_lsh-in-higher-dimensions.mp4
01_introduction-to-nearest-neighbor-search-and-algorithms
02_retrieval-as-k-nearest-neighbor-search.en.srt
04_k-nn-algorithm.en.srt
01_slides-presented-in-this-module_instructions.html
02_retrieval-as-k-nearest-neighbor-search.en.txt
03_1-nn-algorithm.en.txt
04_k-nn-algorithm.en.txt
03_1-nn-algorithm.en.srt
04_k-nn-algorithm.mp4
01_slides-presented-in-this-module_retrieval-intro-annotated.pdf
02_retrieval-as-k-nearest-neighbor-search.mp4
03_1-nn-algorithm.mp4
02_the-importance-of-data-representations-and-distance-metrics
05_to-normalize-or-not-and-other-distance-considerations.en.srt
04_distance-metrics-cosine-similarity.en.srt
02_distance-metrics-euclidean-and-scaled-euclidean.en.srt
01_document-representation.en.srt
03_writing-scaled-euclidean-distance-using-weighted-inner-products.en.txt
05_to-normalize-or-not-and-other-distance-considerations.en.txt
04_distance-metrics-cosine-similarity.en.txt
02_distance-metrics-euclidean-and-scaled-euclidean.en.txt
01_document-representation.en.txt
03_writing-scaled-euclidean-distance-using-weighted-inner-products.en.srt
05_to-normalize-or-not-and-other-distance-considerations.mp4
02_distance-metrics-euclidean-and-scaled-euclidean.mp4
04_distance-metrics-cosine-similarity.mp4
01_document-representation.mp4
03_writing-scaled-euclidean-distance-using-weighted-inner-products.mp4
07_summarizing-nearest-neighbor-search
01_a-brief-recap.en.txt
01_a-brief-recap.en.srt
01_a-brief-recap.mp4
04_mixture-models
07_programming-assignment-2
01_clustering-text-data-with-gaussian-mixtures_instructions.html
01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.json.zip
01_clustering-text-data-with-gaussian-mixtures_people_wiki.sframe.zip
01_clustering-text-data-with-gaussian-mixtures_people_wiki.gl.zip
01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.gl.zip
01_clustering-text-data-with-gaussian-mixtures_people_wiki.csv.zip
01_clustering-text-data-with-gaussian-mixtures_sklearn.feature_extraction.text.TfidfVectorizer.html
01_clustering-text-data-with-gaussian-mixtures_CLU04-NB02.ipynb.zip
01_clustering-text-data-with-gaussian-mixtures_em_utilities.py.zip
01_clustering-text-data-with-gaussian-mixtures_4_tf_idf.npz.zip
02_mixtures-of-gaussians-for-clustering
03_scaling-mixtures-of-gaussians-for-document-clustering.en.srt
01_mixture-of-gaussians.en.srt
02_interpreting-the-mixture-of-gaussian-terms.en.srt
01_mixture-of-gaussians.en.txt
03_scaling-mixtures-of-gaussians-for-document-clustering.en.txt
02_interpreting-the-mixture-of-gaussian-terms.en.txt
01_mixture-of-gaussians.mp4
03_scaling-mixtures-of-gaussians-for-document-clustering.mp4
02_interpreting-the-mixture-of-gaussian-terms.mp4
06_programming-assignment-1
01_implementing-em-for-gaussian-mixtures_instructions.html
01_implementing-em-for-gaussian-mixtures_chosen_images.png
01_implementing-em-for-gaussian-mixtures_LinearAlgebraReview.html
01_implementing-em-for-gaussian-mixtures_scipy.stats.multivariate_normal.html
01_implementing-em-for-gaussian-mixtures_CLU04-NB01.ipynb.zip
01_implementing-em-for-gaussian-mixtures_images.zip
01_implementing-em-for-gaussian-mixtures_images.sf.zip
01_motivating-and-setting-the-foundation-for-mixture-models
02_motiving-probabilistic-clustering-models.en.srt
05_bivariate-and-multivariate-gaussians.en.srt
03_aggregating-over-unknown-classes-in-an-image-dataset.en.srt
02_motiving-probabilistic-clustering-models.en.txt
05_bivariate-and-multivariate-gaussians.en.txt
01_slides-presented-in-this-module_instructions.html
04_univariate-gaussian-distributions.en.txt
03_aggregating-over-unknown-classes-in-an-image-dataset.en.txt
04_univariate-gaussian-distributions.en.srt
02_motiving-probabilistic-clustering-models.mp4
03_aggregating-over-unknown-classes-in-an-image-dataset.mp4
05_bivariate-and-multivariate-gaussians.mp4
01_slides-presented-in-this-module_mixmodel-EM-annotated.pdf
04_univariate-gaussian-distributions.mp4
04_the-em-algorithm
04_optional-a-worked-out-example-for-em_instructions.html
02_convergence-initialization-and-overfitting-of-em.en.srt
02_convergence-initialization-and-overfitting-of-em.en.txt
01_em-iterates-in-equations-and-pictures.en.srt
03_relationship-to-k-means.en.txt
01_em-iterates-in-equations-and-pictures.en.txt
03_relationship-to-k-means.en.srt
02_convergence-initialization-and-overfitting-of-em.mp4
01_em-iterates-in-equations-and-pictures.mp4
03_relationship-to-k-means.mp4
03_expectation-maximization-em-building-blocks
04_estimating-cluster-parameters-from-soft-assignments.en.srt
01_computing-soft-assignments-from-known-cluster-parameters.en.srt
03_estimating-cluster-parameters-from-known-cluster-assignments.en.srt
02_optional-responsibilities-as-bayes-rule.en.srt
04_estimating-cluster-parameters-from-soft-assignments.en.txt
01_computing-soft-assignments-from-known-cluster-parameters.en.txt
03_estimating-cluster-parameters-from-known-cluster-assignments.en.txt
02_optional-responsibilities-as-bayes-rule.en.txt
01_computing-soft-assignments-from-known-cluster-parameters.mp4
04_estimating-cluster-parameters-from-soft-assignments.mp4
03_estimating-cluster-parameters-from-known-cluster-assignments.mp4
02_optional-responsibilities-as-bayes-rule.mp4
05_summarizing-mixture-models
01_a-brief-recap.en.srt
01_a-brief-recap.en.txt
01_a-brief-recap.mp4
05_mixed-membership-modeling-via-latent-dirichlet-allocation
05_programming-assignment
01_modeling-text-topics-with-latent-dirichlet-allocation_topic_models.zip
01_modeling-text-topics-with-latent-dirichlet-allocation_people_wiki.sframe.zip
01_modeling-text-topics-with-latent-dirichlet-allocation_CLU05-NB01.ipynb.zip
01_modeling-text-topics-with-latent-dirichlet-allocation_instructions.html
03_collapsed-gibbs-sampling-for-lda
03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.srt
03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.txt
04_using-the-output-of-collapsed-gibbs-sampling.en.srt
01_what-is-collapsed-gibbs-sampling.en.txt
02_a-worked-example-for-lda-initial-setup.en.srt
01_what-is-collapsed-gibbs-sampling.en.srt
04_using-the-output-of-collapsed-gibbs-sampling.en.txt
02_a-worked-example-for-lda-initial-setup.en.txt
03_a-worked-example-for-lda-deriving-the-resampling-distribution.mp4
04_using-the-output-of-collapsed-gibbs-sampling.mp4
01_what-is-collapsed-gibbs-sampling.mp4
02_a-worked-example-for-lda-initial-setup.mp4
02_bayesian-inference-via-gibbs-sampling
03_a-standard-gibbs-sampler-for-lda.en.srt
02_gibbs-sampling-from-10-000-feet.en.srt
03_a-standard-gibbs-sampler-for-lda.en.txt
01_the-need-for-bayesian-inference.en.srt
02_gibbs-sampling-from-10-000-feet.en.txt
01_the-need-for-bayesian-inference.en.txt
03_a-standard-gibbs-sampler-for-lda.mp4
02_gibbs-sampling-from-10-000-feet.mp4
01_the-need-for-bayesian-inference.mp4
01_introduction-to-latent-dirichlet-allocation
05_goal-of-lda-inference.en.srt
03_an-alternative-document-clustering-model.en.srt
01_slides-presented-in-this-module_instructions.html
04_components-of-latent-dirichlet-allocation-model.en.txt
02_mixed-membership-models-for-documents.en.srt
05_goal-of-lda-inference.en.txt
03_an-alternative-document-clustering-model.en.txt
04_components-of-latent-dirichlet-allocation-model.en.srt
02_mixed-membership-models-for-documents.en.txt
05_goal-of-lda-inference.mp4
03_an-alternative-document-clustering-model.mp4
02_mixed-membership-models-for-documents.mp4
04_components-of-latent-dirichlet-allocation-model.mp4
01_slides-presented-in-this-module_LDA-annotated.pdf
04_summarizing-latent-dirichlet-allocation
01_a-brief-recap.en.srt
01_a-brief-recap.en.txt
01_a-brief-recap.mp4
03_clustering-with-k-means
03_programming-assignment
01_clustering-text-data-with-k-means_people_wiki.sframe.zip
01_clustering-text-data-with-k-means_people_wiki.gl.zip
01_clustering-text-data-with-k-means_instructions.html
01_clustering-text-data-with-k-means_sklearn.preprocessing.normalize.html
01_clustering-text-data-with-k-means_people_wiki_tf_idf.npz.zip
01_clustering-text-data-with-k-means_kmeans-arrays.npz.zip
01_clustering-text-data-with-k-means_people_wiki.csv.zip
01_clustering-text-data-with-k-means_sklearn.feature_extraction.text.TfidfVectorizer.html
01_clustering-text-data-with-k-means_sklearn.cluster.KMeans.html
01_clustering-text-data-with-k-means_CLU03-NB01.ipynb.zip
01_clustering-text-data-with-k-means_numpy.mean.html
01_clustering-text-data-with-k-means_numpy.argmin.html
01_clustering-text-data-with-k-means_pyplot_api.html
01_clustering-text-data-with-k-means_people_wiki_map_index_to_word.json.zip
01_clustering-text-data-with-k-means_people_wiki_map_index_to_word.gl.zip
02_clustering-via-k-means
04_assessing-the-quality-and-choosing-the-number-of-clusters.en.srt
02_k-means-as-coordinate-descent.en.srt
01_the-k-means-algorithm.en.srt
04_assessing-the-quality-and-choosing-the-number-of-clusters.en.txt
02_k-means-as-coordinate-descent.en.txt
03_smart-initialization-via-k-means.en.srt
01_the-k-means-algorithm.en.txt
03_smart-initialization-via-k-means.en.txt
04_assessing-the-quality-and-choosing-the-number-of-clusters.mp4
02_k-means-as-coordinate-descent.mp4
01_the-k-means-algorithm.mp4
03_smart-initialization-via-k-means.mp4
04_mapreduce-for-scaling-k-means
01_motivating-mapreduce.en.srt
04_mapreduce-for-k-means.en.srt
03_mapreduce-execution-overview-and-combiners.en.srt
01_motivating-mapreduce.en.txt
02_the-general-mapreduce-abstraction.en.srt
04_mapreduce-for-k-means.en.txt
03_mapreduce-execution-overview-and-combiners.en.txt
02_the-general-mapreduce-abstraction.en.txt
01_motivating-mapreduce.mp4
04_mapreduce-for-k-means.mp4
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01_modeling-text-data-with-a-hierarchy-of-clusters_CLU06-NB01.ipynb.zip
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01_what-we-ve-learned
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02_thank-you.en.srt
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01_what-we-didn-t-cover.mp4
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01_welcome
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ml-classification
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06_programming-assignment
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01_linear-classifiers
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05_decision-trees
07_programming-assignment-2
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06_programming-assignment-1
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01_intuition-behind-decision-trees
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08_boosting
04_programming-assignment-1
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07_programming-assignment-2
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02_adaboost
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07_handling-missing-data
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04_overfitting-regularization-in-logistic-regression
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06_programming-assignment
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10_scaling-to-huge-datasets-online-learning
07_programming-assignment
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Torrent description
Feel free to post any comments about this torrent, including links to Subtitle, samples, screenshots, or any other relevant information, Watch Machine Learning Specialization Online Free Full Movies Like 123Movies, Putlockers, Fmovies, Netflix or Download Direct via Magnet Link in Torrent Details.
