Machine Learning Specialization

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


     TutsNode.net.txt -


     01_slides-presented-in-this-module_LM-3dtexton.pdf -


     01_welcome.en.txt -


     01_predicting-house-prices-using-k-nearest-neighbors-regression_numpy.argsort.html -


     02_assessing-performance-intro.en.txt -


     0 -


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


     07_optional-a-worked-out-example-for-kd-trees_instructions.html -


     01_predicting-sentiment-from-product-reviews_sklearn.linear_model.LogisticRegression.html -


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


     02_deep-learning_exam.html -


     01_clustering-text-data-with-gaussian-mixtures_instructions.html -


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     01_recommending-songs-assignment_song_data.csv -


     01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.json.zip -


     01_implementing-binary-decision-trees_instructions.html -


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     01_retrieving-wikipedia-articles-assignment_people_wiki.csv -


     01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_home_data.sframe.zip -


     01_deep-features-for-image-retrieval-assignment_nearest_neighbors.html -


     02_formally-deriving-why-3-sources-of-error.en.srt -


     06_a-brief-recap.en.srt -


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     01_deep-features-for-image-retrieval-assignment_image_test_data.csv -


     01_predicting-house-prices-assignment_home_data.sframe.zip -


     02_clustering-and-similarity_exam.html -


     03_scaling-mixtures-of-gaussians-for-document-clustering.en.srt -


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     01_modeling-text-topics-with-latent-dirichlet-allocation_topic_models.zip -


     01_implementing-locality-sensitive-hashing-from-scratch_instructions.html -


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


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     01_analyzing-product-sentiment-assignment_amazon_baby.csv -


     01_slides-presented-in-this-module_eccv06.pdf -


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


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     01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.sframe.zip -


     02_searching-for-images-a-case-study-in-deep-learning.mp4 -


     01_exploring-ensemble-methods_sklearn.ensemble.GradientBoostingClassifier.html -


     01_slides-presented-in-this-module_instructions.html -


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     01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.sframe.zip -


     01_slides-presented-in-this-module_decision-trees-missing-values-annotated.pdf -


     03_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip -


     01_recap-of-overfitting-regularization-in-logistic-regression.en.srt -


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     01_clustering-text-data-with-k-means_people_wiki.sframe.zip -


     01_slides-presented-in-this-module_imagenet.pdf -


     01_predicting-house-prices-using-k-nearest-neighbors-regression_home_data_small.sframe.zip -


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


     02_k-nearest-neighbors-in-practice.en.txt -


     10 -


     01_clustering-text-data-with-gaussian-mixtures_people_wiki.sframe.zip -


     02_multiple-regression-intro.mp4 -


     01_slides-presented-in-this-module_iccv99.pdf -


     01_getting-started-with-python-jupyter-notebook-turi-create_Turi_Getting_Started_with_SFrames.ipynb.zip -


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     01_modeling-text-topics-with-latent-dirichlet-allocation_people_wiki.sframe.zip -


     01_logistic-regression-with-l2-regularization_module-4-assignment-numpy-arrays.npz.zip -


     01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_train_valid_shuffled.csv.zip -


     04_a-brief-recap.en.txt -


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     01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.sframe.zip -


     01_training-logistic-regression-via-stochastic-gradient-ascent_module-10-assignment-numpy-arrays.npz.zip -


     01_slides-presented-in-this-module_Dalal-cvpr05.pdf -


     06_reading-software-tools-you-ll-need_quickstart.html -


     05_impact-of-classification.en.srt -


     01_multiclass.en.txt -


     01_slides-presented-in-this-module_precision-recall.pdf -


     01_practice-quiz-multiple-linear-regression_exam.html -


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     01_download-wiki-people-data_people_wiki.sframe.zip -


     01_implementing-logistic-regression-from-scratch_module-3-assignment-numpy-arrays.npz.zip -


     01_polynomial-regression_wk3_kc_house_train_data.csv.zip -


     01_deep-features-for-image-retrieval-assignment_tabular-data.html -


     03_collaborative-filtering-algorithm.en.srt -


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     01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_people_wiki.sframe.zip -


     01_fitting-a-simple-linear-regression-model-on-housing-data_home_data.sframe.zip -


     02_recommender-systems_exam.html -


     01_boosting-a-decision-stump_instructions.html -


     04_using-probabilities-in-classification.en.txt -


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     01_retrieving-wikipedia-articles-assignment_people_wiki.sframe.zip -


     01_exploring-different-multiple-regression-models-for-house-price-prediction_home_data.sframe.zip -


     01_implementing-em-for-gaussian-mixtures_instructions.html -


     01_practice-quiz-gradient-descent-for-logistic-regression_exam.html -


     03_test-error-what-we-can-actually-compute.en.srt -


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     01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.gl.zip -


     01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_train_data.csv.zip -


     01_polynomial-regression_wk3_kc_house_test_data.csv.zip -


     02_multiclass-classification-with-decision-trees.en.srt -


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     01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.gl.zip -


     01_using-lasso-to-select-features_wk3_kc_house_train_data.csv.zip -


     01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_test_data.csv.zip -


     01_recommender-systems-ml-block-diagram.en.txt -


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     01_clustering-text-data-with-k-means_people_wiki.gl.zip -


     01_implementing-em-for-gaussian-mixtures_chosen_images.png -


     01_using-lasso-to-select-features_wk3_kc_house_test_data.csv.zip -


     02_outline-of-first-half-of-course.en.txt -


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     01_clustering-text-data-with-gaussian-mixtures_people_wiki.gl.zip -


     01_deep-features-for-image-retrieval-assignment_instructions.html -


     01_clustering-text-data-with-k-means_instructions.html -


     02_motiving-probabilistic-clustering-models.en.srt -


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     01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.gl.zip -


     01_polynomial-regression_wk3_kc_house_valid_data.csv.zip -


     01_practice-quiz-the-problem-of-overfitting_exam.html -


     01_getting-started-with-python-jupyter-notebook-turi-create_people-example.csv -


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     07_optional-improving-efficiency-through-multiple-tables.mp4 -


     02_assessing-performance-intro.mp4 -


     01_deep-features-for-image-retrieval-assignment_turicreate.Sketch.html -


     02_you-ve-made-it.en.srt -


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     01_deep-features-for-image-retrieval-assignment_image_train_data.csv -


     01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_valid_data.csv.zip -


     01_practice-quiz-cost-function-for-logistic-regression_exam.html -


     03_using-the-fitted-line.en.txt -


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     01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_tf_idf.npz.zip -


     02_implementing-gradient-descent-for-multiple-regression_home_data.sframe.zip -


     01_deep-features-for-image-retrieval-assignment_nearest_neighbors.md -


     01_clustering-text-data-with-k-means_sklearn.preprocessing.normalize.html -


     24 -


     01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_tf_idf.npz.zip -


     01_polynomial-regression_home_data.sframe.zip -


     01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_train.csv.zip -


     01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.metrics.pairwise.euclidean_distances.html -


     02_a-case-study-in-predicting-house-prices.en.txt -


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     01_clustering-text-data-with-k-means_people_wiki_tf_idf.npz.zip -


     01_observing-effects-of-l2-penalty-in-polynomial-regression_home_data.sframe.zip -


     07_software-tools-you-ll-need-for-this-course_quickstart.html -


     03_demand-prediction.en.srt -


     02_thank-you.en.txt -


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     01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki_tf_idf.npz.zip -


     01_implementing-ridge-regression-via-gradient-descent_home_data.sframe.zip -


     08_reading-software-tools-you-ll-need_quickstart.html -


     02_computation-graph-optional.en.srt -


     01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html -


     27 -


     01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_test_data.zip -


     01_using-lasso-to-select-features_home_data.sframe.zip -


     04_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip -


     03_what-you-ll-be-able-to-do.en.txt -


     28 -


     01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_test_data.zip -


     01_implementing-lasso-using-coordinate-descent_home_data.sframe.zip -


     04_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip -


     02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.en.srt -


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     01_deep-features-for-image-retrieval-assignment_image_test_data.zip -


     01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_data.csv.zip -


     01_choosing-features-and-metrics-for-nearest-neighbor-search_stdtypes.html -


     07_optional-improving-efficiency-through-multiple-tables.en.srt -


     06_effect-of-coefficient-values-on-decision-boundary.en.srt -


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     01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_song_data.sframe.zip -


     01_exploring-ensemble-methods_instructions.html -


     03_computing-coefficient-of-each-ensemble-component.en.txt -


     31 -


     01_recommending-songs-assignment_song_data.sframe.zip -


     01_implementing-logistic-regression-from-scratch_instructions.html -


     04_defining-more-bins.en.srt -


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     01_clustering-text-data-with-k-means_kmeans-arrays.npz.zip -


     01_using-lasso-to-select-features_wk3_kc_house_valid_data.csv.zip -


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


     04_linear-regression-a-model-based-approach.en.txt -


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     02_formally-deriving-why-3-sources-of-error.mp4 -


     01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_data.csv.zip -


     01_exploring-ensemble-methods_boosted_trees_classifier.html -


     01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small.csv.zip -


     03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.srt -


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     01_formally-defining-the-3-sources-of-error.mp4 -


     02_where-should-my-files-go_instructions.html -


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


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     01_deriving-the-lasso-coordinate-descent-update.mp4 -


     02_implementing-gradient-descent-for-multiple-regression_kc_house_data.csv.zip -


     [TGx]Downloaded from torrentgalaxy.to .txt -


     01_exploring-ensemble-methods_graphlab.SFrame.to_numpy.html -


     04_optional-a-worked-out-example-for-em_instructions.html -


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


     01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.CountVectorizer.html -


     03_welcome-to-this-course-and-specialization.en.txt -


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     01_predicting-sentiment-from-product-reviews_amazon_baby.gl.zip -


     01_polynomial-regression_kc_house_data.csv.zip -


     01_observing-effects-of-l2-penalty-in-polynomial-regression_kc_house_data.csv.zip -


     01_implementing-em-for-gaussian-mixtures_LinearAlgebraReview.html -


     01_mean-normalization.en.srt -


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     01_exploring-precision-and-recall_amazon_baby.gl.zip -


     01_implementing-ridge-regression-via-gradient-descent_kc_house_data.csv.zip -


     01_using-lasso-to-select-features_kc_house_data.csv.zip -


     01_training-logistic-regression-via-stochastic-gradient-ascent_instructions.html -


     07_optional-improving-efficiency-through-multiple-tables.en.txt -


     01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_people-example.csv -


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     01_predicting-sentiment-from-product-reviews_amazon_baby.sframe.zip -


     01_implementing-lasso-using-coordinate-descent_kc_house_data.csv.zip -


     02_regression_exam.html -


     02_deep-features-for-image-retrieval_exam.html -


     01_identifying-safe-loans-with-decision-trees_sklearn.tree.DecisionTreeClassifier.html -


     03_a-standard-gibbs-sampler-for-lda.en.srt -


     01_practice-quiz-supervised-vs-unsupervised-learning_exam.html -


     01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_train_data.csv.zip -


     01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_train_data.csv.zip -


     02_implementing-gradient-descent-for-multiple-regression_kc_house_train_data.csv.zip -


     01_polynomial-regression_wk3_kc_house_set_3_data.csv.zip -


     05_querying-for-the-most-similar-images-for-car-image.en.srt -


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     01_exploring-precision-and-recall_amazon_baby.sframe.zip -


     01_implementing-ridge-regression-via-gradient-descent_kc_house_train_data.csv.zip -


     01_implementing-lasso-using-coordinate-descent_kc_house_train_data.csv.zip -


     01_predicting-house-prices-assignment_graphlab.SFrame.html -


     01_practice-quiz-gradient-descent-in-practice_exam.html -


     02_the-boosting-question.en.srt -


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     01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_amazon_baby.sframe.zip -


     01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.gl.zip -


     01_identifying-safe-loans-with-decision-trees_graphlab.SFrame.to_numpy.html -


     01_choosing-features-and-metrics-for-nearest-neighbor-search_graphlab.SFrame.join.html -


     01_recommending-songs-assignment_graphlab.SFrame.groupby.html -


     01_practice-quiz-regression_exam.html -


     01_predicting-sentiment-from-product-reviews_module-2-assignment-train-idx.json.zip -


     01_exploring-precision-and-recall_instructions.html -


     01_computing-the-gradient-of-rss.en.srt -


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     01_analyzing-product-sentiment-assignment_amazon_baby.sframe.zip -


     01_exploring-precision-and-recall_module-9-assignment-train-idx.json.zip -


     01_numpy-tutorial_quickstart.html -


     01_implementing-locality-sensitive-hashing-from-scratch_itertools.html -


     01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_3_data.csv.zip -


     01_polynomial-regression_wk3_kc_house_set_1_data.csv.zip -


     01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_1_data.csv.zip -


     01_polynomial-regression_wk3_kc_house_set_4_data.csv.zip -


     01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_4_data.csv.zip -


     01_polynomial-regression_wk3_kc_house_set_2_data.csv.zip -


     01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_2_data.csv.zip -


     01_analyzing-product-sentiment-assignment_datastructures.html -


     01_predicting-house-prices-using-k-nearest-neighbors-regression_instructions.html -


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


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     01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.csv.zip -


     01_logistic-regression-with-l2-regularization_instructions.html -


     01_logistic-regression-with-l2-regularization_module-4-assignment-train-idx.json.zip -


     03_creating-the-word-count-vector.en.txt -


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     01_clustering-text-data-with-k-means_people_wiki.csv.zip -


     01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND02-NB01.ipynb.zip -


     01_predicting-house-prices-assignment_FND02-NB01.ipynb.zip -


     02_retrieval-as-k-nearest-neighbor-search.en.srt -


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     01_clustering-text-data-with-gaussian-mixtures_people_wiki.csv.zip -


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


     01__MentorProgramInformation.pdf -


     01_gradient-descent.en.srt -


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     01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.csv.zip -


     01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.TfidfVectorizer.html -


     01_implementing-locality-sensitive-hashing-from-scratch_sklearn.feature_extraction.text.TfidfVectorizer.html -


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


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


     01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_test_data.csv.zip -


     01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_test_data.csv.zip -


     05_feature-engineering.en.txt -


     02_implementing-gradient-descent-for-multiple-regression_kc_house_test_data.csv.zip -


     01_implementing-ridge-regression-via-gradient-descent_kc_house_test_data.csv.zip -


     01_implementing-lasso-using-coordinate-descent_kc_house_test_data.csv.zip -


     01_clustering-text-data-with-k-means_sklearn.feature_extraction.text.TfidfVectorizer.html -


     01_clustering-text-data-with-gaussian-mixtures_sklearn.feature_extraction.text.TfidfVectorizer.html -


     01_week-2-practice-lab-linear-regression_instructions.html -


     01_modeling-text-data-with-a-hierarchy-of-clusters_sklearn.feature_extraction.text.TfidfVectorizer.html -


     01_clustering-text-data-with-k-means_sklearn.cluster.KMeans.html -


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


     01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_test.csv.zip -


     01_identifying-safe-loans-with-decision-trees_module-5-assignment-1-train-idx.json.zip -


     01_implementing-binary-decision-trees_module-5-assignment-2-train-idx.json.zip -


     01_boosting-a-decision-stump_module-8-assignment-2-train-idx.json.zip -


     01_decision-trees-in-practice_module-6-assignment-train-idx.json.zip -


     01_exploring-ensemble-methods_module-8-assignment-1-train-idx.json.zip -


     01_training-logistic-regression-via-stochastic-gradient-ascent_module-10-assignment-train-idx.json.zip -


     01_practice-quiz-classification-with-logistic-regression_exam.html -


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


     01_predicting-sentiment-from-product-reviews_module-2-assignment-test-idx.json.zip -


     01_exploring-precision-and-recall_module-9-assignment-test-idx.json.zip -


     01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_validation.csv.zip -


     02_classification_exam.html -


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     02_where-is-ml-going.mp4 -


     01_download-notebooks-to-follow-along_PhillyCrime.ipynb.zip -


     01_decision-trees-in-practice_instructions.html -


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


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


     05_optional-reading-worked-out-example-for-gradient-descent_instructions.html -


     01_predicting-house-prices-assignment_instructions.html -


     05_prioritizing-important-words-with-tf-idf.en.srt -


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


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


     01_identifying-safe-loans-with-decision-trees_instructions.html -


     01_predicting-sentiment-from-product-reviews_instructions.html -


     02_pca-algorithm-optional.en.srt -


     01_practice-quiz-train-the-model-with-gradient-descent_exam.html -


     01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND05-NB01.ipynb.zip -


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     02_applications-of-machine-learning.mp4 -


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


     02_training-details.en.srt -


     01_week-3-practice-lab-logistic-regression_instructions.html -


     01_modeling-text-data-with-a-hierarchy-of-clusters_instructions.html -


     01_deriving-the-lasso-coordinate-descent-update.en.srt -


     04_cost-function-intuition.en.srt -


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


     01_important-update-regarding-the-machine-learning-specialization_instructions.html -


     02_slides-presented-in-this-module_instructions.html -


     04_learning-curves.en.srt -


     03_welcome-to-the-classification-course-a-part-of-the-machine-learning.en.srt -


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     02_loading-exploring-wikipedia-data.mp4 -


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     03_ensemble-classifiers.mp4 -


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     02_error-analysis.mp4 -


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     01_the-k-means-algorithm.mp4 -


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     03_scaling-mixtures-of-gaussians-for-document-clustering.mp4 -


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     02_more-complex-neural-networks.mp4 -


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     01_the-need-for-bayesian-inference.mp4 -


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     01_em-iterates-in-equations-and-pictures.mp4 -


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     03_nn-search-with-kd-trees.mp4 -


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     01_advanced-optimization.mp4 -


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     03_the-lasso-objective-and-its-coefficient-path.mp4 -


     322 -


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     323 -


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     324 -


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     05_computing-distances-between-wikipedia-articles.mp4 -


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     01_encoding-categorical-inputs.mp4 -


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     01_principle-of-occams-razor-learning-simpler-decision-trees.mp4 -


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     02_using-stochastic-gradient-for-online-learning.mp4 -


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     02_interpreting-the-mixture-of-gaussian-terms.mp4 -


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     02_who-is-this-specialization-for.mp4 -


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     02_sampling-with-replacement.mp4 -


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     04_overfitting-in-classification.mp4 -


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     02_distance-metrics.mp4 -


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     02_recommendations-from-known-user-item-features.mp4 -


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


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     01_motivating-the-use-of-multiple-inputs.mp4 -


     370 -


     01_overfitting-in-classifiers-leads-to-overconfident-predictions.mp4 -


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     01_document-representation.mp4 -


     372 -


     02_approach-1-closed-form-solution.mp4 -


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     01_slides-presented-in-this-module_clustering-intro-annotated.pdf -


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     01_gradients-derivatives-in-multiple-dimensions.mp4 -


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     02_learning-boosted-decision-stumps-with-adaboost.mp4 -


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     02_thresholding-ridge-coefficients.mp4 -


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     03_what-do-we-mean-by-loss.mp4 -


     380 -


     03_weighted-k-nearest-neighbors.mp4 -


     381 -


     03_what-is-good-performance-for-a-classifier.mp4 -


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     01_implementing-em-for-gaussian-mixtures_images.zip -


     383 -


     02_starting-a-jupyter-notebook.mp4 -


     384 -


     03_example-of-computing-derivative-for-logistic-regression.mp4 -


     385 -


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     04_interpreting-derivative-for-logistic-regression.mp4 -


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     03_all-subsets.mp4 -


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     01_feature-scaling-part-1.mp4 -


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     02_the-resulting-ridge-objective-and-its-extreme-solutions.mp4 -


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     01_slides-presented-in-this-module_retrieval-intro-annotated.pdf -


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     03_visualizing-effect-of-l2-regularization-in-logistic-regression.mp4 -


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     04_complexity-of-nn-search-with-kd-trees.mp4 -


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     05_prioritizing-important-words-with-tf-idf.mp4 -


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     04_other-regression-examples.mp4 -


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     04_building-a-recommender-system-via-classification.mp4 -


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     04_matrix-multiplication-code.mp4 -


     399 -


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     02_mixed-membership-models-for-documents.mp4 -


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


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     03_visualizing-decision-boundaries.mp4 -


     407 -


     08_applying-model-to-find-most-positive-negative-reviews-for-a-product.mp4 -


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     03_creating-evaluating-a-popularity-based-song-recommender.mp4 -


     409 -


     02_weighted-error.mp4 -


     410 -


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     02_simple-and-multiple-regression.mp4 -


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     01_training-and-evaluating-a-classifier.mp4 -


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     07_examples-of-document-retrieval-in-action.mp4 -


     414 -


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


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     03_strategy-1-purification-by-skipping-missing-data.mp4 -


     418 -


     02_learning-a-decision-stump.mp4 -


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     01_very-optional-deriving-gradient-of-logistic-regression-log-trick.mp4 -


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     02_lsh-as-an-alternative-to-kd-trees.mp4 -


     421 -


     05_the-future-of-intelligent-applications.mp4 -


     422 -


     01_gradient-descent-implementation.mp4 -


     423 -


     01_implementing-logistic-regression-from-scratch_amazon_baby_subset.sframe.zip -


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     01_logistic-regression-with-l2-regularization_amazon_baby_subset.sframe.zip -


     425 -


     01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.sframe.zip -


     426 -


     01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.gl.zip -


     427 -


     01_implementing-logistic-regression-from-scratch_amazon_baby_subset.gl.zip -


     428 -


     01_logistic-regression-with-l2-regularization_amazon_baby_subset.gl.zip -


     429 -


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     430 -


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     433 -


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     442 -


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     443 -


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     444 -


     02_k-means-intuition.mp4 -


     445 -


     01_slides-presented-in-this-module_recommenders-intro-annotated.pdf -


     446 -


     03_intuition-behind-linear-classifiers.mp4 -


     447 -


     01_recommender-systems-ml-block-diagram.mp4 -


     448 -


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


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     467 -


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     01_slides-presented-in-this-module_logistic-regression-model-annotated.pdf -


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     03_intuition-behind-maximum-likelihood-estimation.mp4 -


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     04_task-of-learning-decision-trees-from-data.mp4 -


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     01_slides-presented-in-this-module_kmeans-annotated.pdf -


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     06_optional-adding-regularization.mp4 -


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     01_slides-presented-in-this-module_deeplearning-annotated.pdf -


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     02_slides-presented-in-this-module_intro.pdf -


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     01_slides-presented-in-this-module_week2_multipleregression-annotated.pdf -


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     577 -


     06_regression-with-general-features-of-1-input.mp4 -


     578 -


     01_what-we-didn-t-cover.mp4 -


     579 -


     05_finding-the-min-via-hill-descent.mp4 -


     580 -


     04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.mp4 -


     581 -


     06_complexity-of-the-greedy-forward-stepwise-algorithm.mp4 -


     582 -


     01_what-makes-the-lasso-objective-different.mp4 -


     583 -


     01_multiclass.mp4 -


     584 -


     01_welcome.mp4 -


     585 -


     05_unsupervised-learning-part-2.mp4 -


     586 -


     04_thank-you.mp4 -


     587 -


     02_effect-of-popular-items.mp4 -


     588 -


     02_learning-algorithm-for-logistic-regression.mp4 -


     589 -


     02_loading-exploring-product-review-data.mp4 -


     590 -


     01_slides-presented-in-this-module_week1_simpleregression-annotated.pdf -


     591 -


     03_demo-of-deep-learning-model-on-imagenet-data.mp4 -


     592 -


     05_overview-of-learning-logistic-regression-models.mp4 -


     593 -


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


     594 -


     05_feature-engineering.mp4 -


     595 -


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


     596 -


     01_adaboost-overview.mp4 -


     597 -


     09_learning-a-model-to-predict-house-prices-from-more-features.mp4 -


     598 -


     01_why-hierarchical-clustering.mp4 -


     599 -


     06_effect-of-coefficient-values-on-decision-boundary.mp4 -


     600 -


     01_the-simple-linear-regression-model.mp4 -


     601 -


     03_predictions-in-matrix-form.mp4 -


     602 -


     02_loading-image-data.mp4 -


     603 -


     03_maximizing-a-1d-function-a-worked-example.mp4 -


     604 -


     04_regression-fundamentals-the-task.mp4 -


     605 -


     01_computing-the-gradient-of-rss.mp4 -


     606 -


     02_a-review-of-overfitting.mp4 -


     607 -


     02_thank-you.mp4 -


     608 -


     02_convergence-paths.mp4 -


     609 -


     02_very-optional-expressing-the-log-likelihood.mp4 -


     610 -


     02_multiclass-classification-with-decision-trees.mp4 -


     611 -


     01_recap-of-learning-logistic-regression-classifiers.mp4 -


     612 -


     01_slides-presented-in-this-module_LDA-annotated.pdf -


     613 -


     02_symptoms-of-overfitting-in-polynomial-regression.mp4 -


     614 -


     01_slides-presented-in-this-module_classification-annotated.pdf -


     615 -


     04_complexity-of-all-subsets.mp4 -


     616 -


     01_slides-presented-in-this-module_week3_assessingperformance-annotated.pdf -


     617 -


     03_1-nn-algorithm.mp4 -


     618 -


     05_normalizing-weights.mp4 -


     619 -


     04_other-examples-of-deep-learning-in-computer-vision.mp4 -


     620 -


     03_adding-other-features.mp4 -


     621 -


     03_k-nn-for-classification.mp4 -


     622 -


     01_a-brief-recap.mp4 -


     623 -


     03_what-is-the-document-retrieval-task.mp4 -


     624 -


     02_slides-presented-in-this-module_intro.pdf -


     625 -


     02_slides-presented-in-this-module_intro.pdf -


     626 -


     06_comparing-the-approaches.mp4 -


     627 -


     03_creating-the-word-count-vector.mp4 -


     628 -


     01_recap-of-handling-missing-data.mp4 -


     629 -


     03_splitting-the-data-into-training-and-test-sets.mp4 -


     630 -


     05_evaluating-error-rmse-of-the-simple-model.mp4 -


     631 -


     05_summary-of-gradient-ascent-for-logistic-regression.mp4 -


     632 -


     04_defining-overfitting.mp4 -


     633 -


     05_training-test-split.mp4 -


     634 -


     03_don-t-trust-last-coefficients.mp4 -


     635 -


     01_complexity-of-brute-force-search.mp4 -


     636 -


     03_coordinate-descent-for-lasso-unnormalized-features.mp4 -


     637 -


     03_intuition-behind-decision-trees.mp4 -


     638 -


     01_shuffle-data-before-running-stochastic-gradient.mp4 -


     639 -


     02_optimal-recommenders.mp4 -


     640 -


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


     641 -


     02_thank-you.mp4 -


     642 -


     01_scaling-to-huge-datasets-through-parallelization-module-recap.mp4 -


     643 -


     01_predicting-class-probabilities.mp4 -


     644 -


     04_approach-2-gradient-descent.mp4 -


     645 -


     03_how-ridge-regression-balances-bias-and-variance.mp4 -


     646 -


     04_a-brief-recap.mp4 -


     647 -


     01_recap-of-logistic-regression-classifier.mp4 -


     648 -


     01_recap-of-precision-recall.mp4 -


     649 -


     01_a-brief-recap.mp4 -


     650 -


     01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_map_index_to_word.json.zip -


     651 -


     01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_map_index_to_word.json.zip -


     652 -


     01_clustering-text-data-with-k-means_people_wiki_map_index_to_word.json.zip -


     653 -


     01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki_map_index_to_word.json.zip -


     654 -


     02_a-brief-recap.mp4 -


     655 -


     03_very-optional-deriving-probability-y-1-given-x.mp4 -


     656 -


     03_welcome.mp4 -


     657 -


     01_recap-of-overfitting-and-regularization-in-decision-trees.mp4 -


     658 -


     01_making-predictions-with-decision-trees.mp4 -


     659 -


     03_creating-a-nearest-neighbors-model-for-image-retrieval.mp4 -


     660 -


     04_a-brief-recap.mp4 -


     661 -


     01_predicting-house-prices-assignment_house_images.zip -


     662 -


     01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_house_images.zip -


     663 -


     05_querying-for-the-most-similar-images-for-car-image.mp4 -


     664 -


     03_welcome-to-the-classification-course-a-part-of-the-machine-learning.mp4 -


     665 -


     02_recommender-systems-overview.mp4 -


     666 -


     01_slides-presented-in-this-module_week6_NNkernelregression-annotated.pdf -


     667 -


     06_a-brief-recap.mp4 -


     668 -


     03_what-you-ll-be-able-to-do.mp4 -


     669 -


     01_a-brief-recap.mp4 -


     670 -


     01_slides-presented-in-this-module_closing.pdf -


     671 -


     05_impact-of-classification.mp4 -


     672 -


     01_a-brief-recap.mp4 -


     673 -


     01_recap-of-overfitting-regularization-in-logistic-regression.mp4 -


     674 -


     02_predicting-house-prices-a-case-study-in-regression.mp4 -


     675 -


     02_a-case-study-in-predicting-house-prices.mp4 -


     676 -


     02_a-brief-recap.mp4 -


     677 -


     01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki_map_index_to_word.gl.zip -


     678 -


     01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_map_index_to_word.gl.zip -


     679 -


     01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_map_index_to_word.gl.zip -


     680 -


     01_clustering-text-data-with-k-means_people_wiki_map_index_to_word.gl.zip -


     681 -


     01_slides-presented-in-this-module_online-learning-annotated.pdf -


     682 -


     01_recap-of-decision-trees.mp4 -


     683 -


     07_inspecting-the-model-coefficients-learned.mp4 -


     684 -


     03_welcome-to-this-course-and-specialization.mp4 -


     685 -


     01_clustering-text-data-with-gaussian-mixtures_4_tf_idf.npz.zip -


     686 -


     01_slides-presented-in-this-module_decision-trees-overfitting-annotated.pdf -


     687 -


     01_slides-presented-in-this-module_week4_ridgeregression-annotated.pdf -


     688 -


     01_slides-presented-in-this-module_logistic-regression-learning-annotated.pdf -


     689 -


     02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.mp4 -


     690 -


     01_slides-presented-in-this-module_decision-trees-annotated.pdf -


     691 -


     02_you-ve-made-it.mp4 -


     692 -


     05_lets-get-started.mp4 -


     693 -


     01_slides-presented-in-this-module_logistic-regression-overfitting-annotated.pdf -


     694 -


     01_predicting-house-prices-assignment_home_data.csv -


     695 -


     01_slides-presented-in-this-module_boosting-annotated.pdf -


     696 -


     02_document-retrieval-a-case-study-in-clustering-and-measuring-similarity.mp4 -


     697 -


     01_slides-presented-in-this-module_mikolajczyk_pami05.pdf -

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