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Language:
English
Total Size:
13.2 GB
Info Hash:
1C6FE20E31AFAB2F041D8BC4299B968B95EEE660
Added By:
Added:
July 18, 2023, 7:02 p.m.
Stats:
|
(Last updated: Oct. 10, 2026, 8:25 p.m.)
| File | Size |
|---|---|
| 01_andrew-ng-and-chris-manning-on-natural-language-processing.mp4 | 236.2 MB |
| TutsNode.net.txt | 63 bytes |
| 01_slides-presented-in-this-module_LM-3dtexton.pdf | 1.8 MB |
| 01_welcome.en.txt | 2.8 KB |
| 01_predicting-house-prices-using-k-nearest-neighbors-regression_numpy.argsort.html | 38.8 KB |
| 02_assessing-performance-intro.en.txt | 464 bytes |
| 0 | 16 bytes |
| 01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.mp4 | 230.7 MB |
| 07_optional-a-worked-out-example-for-kd-trees_instructions.html | 1.3 MB |
| 01_predicting-sentiment-from-product-reviews_sklearn.linear_model.LogisticRegression.html | 82.6 KB |
| 1 | 9 bytes |
| 01_andrew-ng-and-fei-fei-li-on-human-centered-ai.mp4 | 214.7 MB |
| 02_deep-learning_exam.html | 1.3 MB |
| 01_clustering-text-data-with-gaussian-mixtures_instructions.html | 21.3 KB |
| 2 | 12 bytes |
| 01_recommending-songs-assignment_song_data.csv | 149.2 MB |
| 01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.json.zip | 834.3 KB |
| 01_implementing-binary-decision-trees_instructions.html | 30.7 KB |
| 3 | 2 bytes |
| 01_retrieving-wikipedia-articles-assignment_people_wiki.csv | 110.9 MB |
| 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_home_data.sframe.zip | 908.2 KB |
| 01_deep-features-for-image-retrieval-assignment_nearest_neighbors.html | 243.2 KB |
| 02_formally-deriving-why-3-sources-of-error.en.srt | 21.8 KB |
| 06_a-brief-recap.en.srt | 1.6 KB |
| 4 | 8 bytes |
| 01_deep-features-for-image-retrieval-assignment_image_test_data.csv | 103.0 MB |
| 01_predicting-house-prices-assignment_home_data.sframe.zip | 908.2 KB |
| 02_clustering-and-similarity_exam.html | 109.2 KB |
| 03_scaling-mixtures-of-gaussians-for-document-clustering.en.srt | 7.0 KB |
| 5 | 2 bytes |
| 01_modeling-text-topics-with-latent-dirichlet-allocation_topic_models.zip | 87.7 MB |
| 01_implementing-locality-sensitive-hashing-from-scratch_instructions.html | 328.0 KB |
| 01_what-is-machine-learning.en.srt | 9.5 KB |
| 6 | 12 bytes |
| 01_analyzing-product-sentiment-assignment_amazon_baby.csv | 85.3 MB |
| 01_slides-presented-in-this-module_eccv06.pdf | 706.4 KB |
| 04_ethical-use-of-recommender-systems.en.txt | 9.0 KB |
| 7 | 1 byte |
| 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.sframe.zip | 56.2 MB |
| 02_searching-for-images-a-case-study-in-deep-learning.mp4 | 1.7 MB |
| 01_exploring-ensemble-methods_sklearn.ensemble.GradientBoostingClassifier.html | 83.4 KB |
| 01_slides-presented-in-this-module_instructions.html | 3.1 KB |
| 8 | 7 bytes |
| 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.sframe.zip | 56.2 MB |
| 01_slides-presented-in-this-module_decision-trees-missing-values-annotated.pdf | 1.6 MB |
| 03_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip | 198.4 KB |
| 01_recap-of-overfitting-regularization-in-logistic-regression.en.srt | 1.3 KB |
| 9 | 2 bytes |
| 01_clustering-text-data-with-k-means_people_wiki.sframe.zip | 56.2 MB |
| 01_slides-presented-in-this-module_imagenet.pdf | 1.3 MB |
| 01_predicting-house-prices-using-k-nearest-neighbors-regression_home_data_small.sframe.zip | 386.9 KB |
| 01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.txt | 31.7 KB |
| 02_k-nearest-neighbors-in-practice.en.txt | 3.2 KB |
| 10 | 3 bytes |
| 01_clustering-text-data-with-gaussian-mixtures_people_wiki.sframe.zip | 56.2 MB |
| 02_multiple-regression-intro.mp4 | 1.2 MB |
| 01_slides-presented-in-this-module_iccv99.pdf | 564.4 KB |
| 01_getting-started-with-python-jupyter-notebook-turi-create_Turi_Getting_Started_with_SFrames.ipynb.zip | 1.7 KB |
| 11 | 7 bytes |
| 01_modeling-text-topics-with-latent-dirichlet-allocation_people_wiki.sframe.zip | 56.2 MB |
| 01_logistic-regression-with-l2-regularization_module-4-assignment-numpy-arrays.npz.zip | 1.2 MB |
| 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_train_valid_shuffled.csv.zip | 610.0 KB |
| 04_a-brief-recap.en.txt | 1.4 KB |
| 12 | 10 bytes |
| 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.sframe.zip | 56.2 MB |
| 01_training-logistic-regression-via-stochastic-gradient-ascent_module-10-assignment-numpy-arrays.npz.zip | 1.2 MB |
| 01_slides-presented-in-this-module_Dalal-cvpr05.pdf | 445.4 KB |
| 06_reading-software-tools-you-ll-need_quickstart.html | 166.3 KB |
| 05_impact-of-classification.en.srt | 1.4 KB |
| 01_multiclass.en.txt | 2.8 KB |
| 01_slides-presented-in-this-module_precision-recall.pdf | 1.9 MB |
| 01_practice-quiz-multiple-linear-regression_exam.html | 69.8 KB |
| 13 | 67 bytes |
| 01_download-wiki-people-data_people_wiki.sframe.zip | 56.2 MB |
| 01_implementing-logistic-regression-from-scratch_module-3-assignment-numpy-arrays.npz.zip | 1.2 MB |
| 01_polynomial-regression_wk3_kc_house_train_data.csv.zip | 354.7 KB |
| 01_deep-features-for-image-retrieval-assignment_tabular-data.html | 243.2 KB |
| 03_collaborative-filtering-algorithm.en.srt | 18.8 KB |
| 14 | 48 bytes |
| 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_people_wiki.sframe.zip | 56.2 MB |
| 01_fitting-a-simple-linear-regression-model-on-housing-data_home_data.sframe.zip | 908.2 KB |
| 02_recommender-systems_exam.html | 789.1 KB |
| 01_boosting-a-decision-stump_instructions.html | 103.4 KB |
| 04_using-probabilities-in-classification.en.txt | 2.1 KB |
| 15 | 2 bytes |
| 01_retrieving-wikipedia-articles-assignment_people_wiki.sframe.zip | 56.2 MB |
| 01_exploring-different-multiple-regression-models-for-house-price-prediction_home_data.sframe.zip | 908.2 KB |
| 01_implementing-em-for-gaussian-mixtures_instructions.html | 786.6 KB |
| 01_practice-quiz-gradient-descent-for-logistic-regression_exam.html | 102.1 KB |
| 03_test-error-what-we-can-actually-compute.en.srt | 5.9 KB |
| 16 | 2 bytes |
| 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.gl.zip | 55.6 MB |
| 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_train_data.csv.zip | 354.7 KB |
| 01_polynomial-regression_wk3_kc_house_test_data.csv.zip | 81.7 KB |
| 02_multiclass-classification-with-decision-trees.en.srt | 3.6 KB |
| 17 | 3 bytes |
| 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.gl.zip | 55.6 MB |
| 01_using-lasso-to-select-features_wk3_kc_house_train_data.csv.zip | 354.7 KB |
| 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_test_data.csv.zip | 81.7 KB |
| 01_recommender-systems-ml-block-diagram.en.txt | 3.6 KB |
| 18 | 10 bytes |
| 01_clustering-text-data-with-k-means_people_wiki.gl.zip | 55.6 MB |
| 01_implementing-em-for-gaussian-mixtures_chosen_images.png | 352.9 KB |
| 01_using-lasso-to-select-features_wk3_kc_house_test_data.csv.zip | 81.7 KB |
| 02_outline-of-first-half-of-course.en.txt | 5.4 KB |
| 19 | 9 bytes |
| 01_clustering-text-data-with-gaussian-mixtures_people_wiki.gl.zip | 55.6 MB |
| 01_deep-features-for-image-retrieval-assignment_instructions.html | 350.4 KB |
| 01_clustering-text-data-with-k-means_instructions.html | 78.8 KB |
| 02_motiving-probabilistic-clustering-models.en.srt | 10.7 KB |
| 20 | 38 bytes |
| 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.gl.zip | 55.6 MB |
| 01_polynomial-regression_wk3_kc_house_valid_data.csv.zip | 350.0 KB |
| 01_practice-quiz-the-problem-of-overfitting_exam.html | 89.8 KB |
| 01_getting-started-with-python-jupyter-notebook-turi-create_people-example.csv | 248 bytes |
| 21 | 48 bytes |
| 07_optional-improving-efficiency-through-multiple-tables.mp4 | 54.2 MB |
| 02_assessing-performance-intro.mp4 | 1.8 MB |
| 01_deep-features-for-image-retrieval-assignment_turicreate.Sketch.html | 28.2 KB |
| 02_you-ve-made-it.en.srt | 1.0 KB |
| 22 | 12 bytes |
| 01_deep-features-for-image-retrieval-assignment_image_train_data.csv | 51.6 MB |
| 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_valid_data.csv.zip | 350.0 KB |
| 01_practice-quiz-cost-function-for-logistic-regression_exam.html | 77.0 KB |
| 03_using-the-fitted-line.en.txt | 3.9 KB |
| 23 | 4 bytes |
| 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_tf_idf.npz.zip | 50.9 MB |
| 02_implementing-gradient-descent-for-multiple-regression_home_data.sframe.zip | 908.2 KB |
| 01_deep-features-for-image-retrieval-assignment_nearest_neighbors.md | 176.1 KB |
| 01_clustering-text-data-with-k-means_sklearn.preprocessing.normalize.html | 18.2 KB |
| 24 | 85 bytes |
| 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_tf_idf.npz.zip | 50.9 MB |
| 01_polynomial-regression_home_data.sframe.zip | 908.2 KB |
| 01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_train.csv.zip | 171.7 KB |
| 01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.metrics.pairwise.euclidean_distances.html | 21.7 KB |
| 02_a-case-study-in-predicting-house-prices.en.txt | 934 bytes |
| 25 | 52 bytes |
| 01_clustering-text-data-with-k-means_people_wiki_tf_idf.npz.zip | 50.9 MB |
| 01_observing-effects-of-l2-penalty-in-polynomial-regression_home_data.sframe.zip | 908.2 KB |
| 07_software-tools-you-ll-need-for-this-course_quickstart.html | 166.3 KB |
| 03_demand-prediction.en.srt | 26.6 KB |
| 02_thank-you.en.txt | 1.4 KB |
| 26 | 22 bytes |
| 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki_tf_idf.npz.zip | 50.9 MB |
| 01_implementing-ridge-regression-via-gradient-descent_home_data.sframe.zip | 908.2 KB |
| 08_reading-software-tools-you-ll-need_quickstart.html | 166.3 KB |
| 02_computation-graph-optional.en.srt | 26.4 KB |
| 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html | 1.6 KB |
| 27 | 4 bytes |
| 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_test_data.zip | 50.9 MB |
| 01_using-lasso-to-select-features_home_data.sframe.zip | 908.2 KB |
| 04_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip | 198.4 KB |
| 03_what-you-ll-be-able-to-do.en.txt | 918 bytes |
| 28 | 1 byte |
| 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_test_data.zip | 50.9 MB |
| 01_implementing-lasso-using-coordinate-descent_home_data.sframe.zip | 908.2 KB |
| 04_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip | 198.4 KB |
| 02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.en.srt | 886 bytes |
| 29 | 33 bytes |
| 01_deep-features-for-image-retrieval-assignment_image_test_data.zip | 50.9 MB |
| 01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_data.csv.zip | 780.7 KB |
| 01_choosing-features-and-metrics-for-nearest-neighbor-search_stdtypes.html | 297.9 KB |
| 07_optional-improving-efficiency-through-multiple-tables.en.srt | 26.0 KB |
| 06_effect-of-coefficient-values-on-decision-boundary.en.srt | 3.0 KB |
| 30 | 14 bytes |
| 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_song_data.sframe.zip | 48.0 MB |
| 01_exploring-ensemble-methods_instructions.html | 31.2 KB |
| 03_computing-coefficient-of-each-ensemble-component.en.txt | 3.7 KB |
| 31 | 1 byte |
| 01_recommending-songs-assignment_song_data.sframe.zip | 48.0 MB |
| 01_implementing-logistic-regression-from-scratch_instructions.html | 30.8 KB |
| 04_defining-more-bins.en.srt | 4.2 KB |
| 32 | 7 bytes |
| 01_clustering-text-data-with-k-means_kmeans-arrays.npz.zip | 47.6 MB |
| 01_using-lasso-to-select-features_wk3_kc_house_valid_data.csv.zip | 350.0 KB |
| 01_andrew-ng-and-chris-manning-on-natural-language-processing.en.txt | 39.9 KB |
| 04_linear-regression-a-model-based-approach.en.txt | 4.1 KB |
| 33 | 12 bytes |
| 02_formally-deriving-why-3-sources-of-error.mp4 | 46.7 MB |
| 01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_data.csv.zip | 780.7 KB |
| 01_exploring-ensemble-methods_boosted_trees_classifier.html | 283.6 KB |
| 01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small.csv.zip | 269.2 KB |
| 03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.srt | 9.5 KB |
| 34 | 4 bytes |
| 01_formally-defining-the-3-sources-of-error.mp4 | 45.2 MB |
| 02_where-should-my-files-go_instructions.html | 757.3 KB |
| 01_reducing-the-number-of-features-optional.en.srt | 18.1 KB |
| 35 | 15 bytes |
| 01_deriving-the-lasso-coordinate-descent-update.mp4 | 42.7 MB |
| 02_implementing-gradient-descent-for-multiple-regression_kc_house_data.csv.zip | 780.7 KB |
| [TGx]Downloaded from torrentgalaxy.to .txt | 585 bytes |
| 01_exploring-ensemble-methods_graphlab.SFrame.to_numpy.html | 283.6 KB |
| 04_optional-a-worked-out-example-for-em_instructions.html | 210.8 KB |
| 01_welcome-to-machine-learning.en.txt | 2.4 KB |
| 01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.CountVectorizer.html | 57.5 KB |
| 03_welcome-to-this-course-and-specialization.en.txt | 637 bytes |
| 36 | 3 bytes |
| 01_predicting-sentiment-from-product-reviews_amazon_baby.gl.zip | 40.3 MB |
| 01_polynomial-regression_kc_house_data.csv.zip | 780.7 KB |
| 01_observing-effects-of-l2-penalty-in-polynomial-regression_kc_house_data.csv.zip | 780.7 KB |
| 01_implementing-em-for-gaussian-mixtures_LinearAlgebraReview.html | 131.9 KB |
| 01_mean-normalization.en.srt | 10.7 KB |
| 37 | 15 bytes |
| 01_exploring-precision-and-recall_amazon_baby.gl.zip | 40.3 MB |
| 01_implementing-ridge-regression-via-gradient-descent_kc_house_data.csv.zip | 780.7 KB |
| 01_using-lasso-to-select-features_kc_house_data.csv.zip | 780.7 KB |
| 01_training-logistic-regression-via-stochastic-gradient-ascent_instructions.html | 126.4 KB |
| 07_optional-improving-efficiency-through-multiple-tables.en.txt | 15.9 KB |
| 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_people-example.csv | 248 bytes |
| 38 | 11 bytes |
| 01_predicting-sentiment-from-product-reviews_amazon_baby.sframe.zip | 40.3 MB |
| 01_implementing-lasso-using-coordinate-descent_kc_house_data.csv.zip | 780.7 KB |
| 02_regression_exam.html | 676.9 KB |
| 02_deep-features-for-image-retrieval_exam.html | 165.1 KB |
| 01_identifying-safe-loans-with-decision-trees_sklearn.tree.DecisionTreeClassifier.html | 74.9 KB |
| 03_a-standard-gibbs-sampler-for-lda.en.srt | 11.4 KB |
| 01_practice-quiz-supervised-vs-unsupervised-learning_exam.html | 1.9 KB |
| 01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_train_data.csv.zip | 628.5 KB |
| 01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_train_data.csv.zip | 628.5 KB |
| 02_implementing-gradient-descent-for-multiple-regression_kc_house_train_data.csv.zip | 628.5 KB |
| 01_polynomial-regression_wk3_kc_house_set_3_data.csv.zip | 158.7 KB |
| 05_querying-for-the-most-similar-images-for-car-image.en.srt | 2.1 KB |
| 39 | 2 bytes |
| 01_exploring-precision-and-recall_amazon_baby.sframe.zip | 40.3 MB |
| 01_implementing-ridge-regression-via-gradient-descent_kc_house_train_data.csv.zip | 628.5 KB |
| 01_implementing-lasso-using-coordinate-descent_kc_house_train_data.csv.zip | 628.5 KB |
| 01_predicting-house-prices-assignment_graphlab.SFrame.html | 281.4 KB |
| 01_practice-quiz-gradient-descent-in-practice_exam.html | 164.9 KB |
| 02_the-boosting-question.en.srt | 5.9 KB |
| 40 | 3 bytes |
| 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_amazon_baby.sframe.zip | 40.3 MB |
| 01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.gl.zip | 623.6 KB |
| 01_identifying-safe-loans-with-decision-trees_graphlab.SFrame.to_numpy.html | 281.4 KB |
| 01_choosing-features-and-metrics-for-nearest-neighbor-search_graphlab.SFrame.join.html | 281.4 KB |
| 01_recommending-songs-assignment_graphlab.SFrame.groupby.html | 281.4 KB |
| 01_practice-quiz-regression_exam.html | 2.6 KB |
| 01_predicting-sentiment-from-product-reviews_module-2-assignment-train-idx.json.zip | 163.9 KB |
| 01_exploring-precision-and-recall_instructions.html | 70.9 KB |
| 01_computing-the-gradient-of-rss.en.srt | 3.7 KB |
| 41 | 1 byte |
| 01_analyzing-product-sentiment-assignment_amazon_baby.sframe.zip | 40.3 MB |
| 01_exploring-precision-and-recall_module-9-assignment-train-idx.json.zip | 163.9 KB |
| 01_numpy-tutorial_quickstart.html | 162.9 KB |
| 01_implementing-locality-sensitive-hashing-from-scratch_itertools.html | 161.2 KB |
| 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_3_data.csv.zip | 158.7 KB |
| 01_polynomial-regression_wk3_kc_house_set_1_data.csv.zip | 158.6 KB |
| 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_1_data.csv.zip | 158.6 KB |
| 01_polynomial-regression_wk3_kc_house_set_4_data.csv.zip | 158.3 KB |
| 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_4_data.csv.zip | 158.3 KB |
| 01_polynomial-regression_wk3_kc_house_set_2_data.csv.zip | 157.6 KB |
| 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_2_data.csv.zip | 157.6 KB |
| 01_analyzing-product-sentiment-assignment_datastructures.html | 93.1 KB |
| 01_predicting-house-prices-using-k-nearest-neighbors-regression_instructions.html | 19.9 KB |
| 42 | 107 bytes |
| 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.csv.zip | 39.9 MB |
| 01_choosing-features-and-metrics-for-nearest-neighbor-search_instructions.html | 123.6 KB |
| 01_what-is-a-derivative-optional.en.txt | 15.2 KB |
| 43 | 178 bytes |
| 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.csv.zip | 39.9 MB |
| 01_logistic-regression-with-l2-regularization_instructions.html | 90.5 KB |
| 01_logistic-regression-with-l2-regularization_module-4-assignment-train-idx.json.zip | 47.1 KB |
| 03_creating-the-word-count-vector.en.txt | 1.3 KB |
| 44 | 16 bytes |
| 01_clustering-text-data-with-k-means_people_wiki.csv.zip | 39.9 MB |
| 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND02-NB01.ipynb.zip | 67.3 KB |
| 01_predicting-house-prices-assignment_FND02-NB01.ipynb.zip | 67.3 KB |
| 02_retrieval-as-k-nearest-neighbor-search.en.srt | 4.4 KB |
| 45 | 4 bytes |
| 01_clustering-text-data-with-gaussian-mixtures_people_wiki.csv.zip | 39.9 MB |
| 01_andrew-ng-and-chris-manning-on-natural-language-processing.en.srt | 63.9 KB |
| 01__MentorProgramInformation.pdf | 62.7 KB |
| 01_gradient-descent.en.srt | 12.2 KB |
| 46 | 2 bytes |
| 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.csv.zip | 39.9 MB |
| 01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.TfidfVectorizer.html | 61.6 KB |
| 01_implementing-locality-sensitive-hashing-from-scratch_sklearn.feature_extraction.text.TfidfVectorizer.html | 61.6 KB |
| 03_the-return-in-reinforcement-learning.en.srt | 15.7 KB |
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| 01_implementing-binary-decision-trees_lending-club-data.gl.zip | 19.4 MB |
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| 01_exploring-ensemble-methods_lending-club-data.gl.zip | 19.4 MB |
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| 01_identifying-safe-loans-with-decision-trees_lending-club-data.sframe.zip | 19.4 MB |
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| 01_boosting-a-decision-stump_lending-club-data.sframe.zip | 19.4 MB |
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| 01_decision-trees-in-practice_lending-club-data.sframe.zip | 19.4 MB |
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| 01_implementing-binary-decision-trees_lending-club-data.sframe.zip | 19.4 MB |
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| 04_gradient-descent-for-multiple-linear-regression.mp4 | 19.4 MB |
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| 01_application-of-deep-learning-to-computer-vision.mp4 | 19.3 MB |
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| 01_k-nearest-neighbors-regression.mp4 | 19.3 MB |
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| 04_random-stochastic-environment-optional.mp4 | 19.3 MB |
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| 01_how-we-got-into-ml.mp4 | 19.1 MB |
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| 04_learning-l2-regularized-logistic-regression-with-gradient-ascent.mp4 | 19.1 MB |
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| 04_transfer-learning-using-data-from-a-different-task.mp4 | 19.0 MB |
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| 02_variance-and-the-bias-variance-tradeoff.mp4 | 19.0 MB |
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| 01_error-metrics-for-skewed-datasets.mp4 | 19.0 MB |
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| 03_decision-boundary.mp4 | 18.9 MB |
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| 01_clustering-and-similarity-ml-block-diagram.mp4 | 18.9 MB |
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| 01_mean-normalization.mp4 | 18.9 MB |
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| 06_regression-trees-optional.mp4 | 18.9 MB |
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| 01_multiple-features.mp4 | 18.9 MB |
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| 03_estimating-cluster-parameters-from-known-cluster-assignments.mp4 | 18.8 MB |
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| 03_exploring-word-counts.mp4 | 18.7 MB |
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| 04_unsupervised-learning-part-1.mp4 | 18.7 MB |
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| 05_greedy-algorithms.mp4 | 18.7 MB |
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| 03_how-to-handle-the-intercept.mp4 | 18.6 MB |
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| 01_from-weighted-k-nn-to-kernel-regression.mp4 | 18.6 MB |
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| 05_linear-classifiers.mp4 | 18.5 MB |
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| 04_mapreduce-for-k-means.mp4 | 18.5 MB |
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| 01_identifying-safe-loans-with-decision-trees_lending-club-data.csv.zip | 18.5 MB |
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| 01_exploring-ensemble-methods_lending-club-data.csv.zip | 18.5 MB |
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| 01_implementing-binary-decision-trees_lending-club-data.csv.zip | 18.5 MB |
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| 01_decision-trees-in-practice_lending-club-data.csv.zip | 18.5 MB |
| 235 | 1.5 MB |
| 01_boosting-a-decision-stump_lending-club-data.csv.zip | 18.5 MB |
| 236 | 1.5 MB |
| 03_what-is-an-intelligent-restaurant-review-system.mp4 | 18.5 MB |
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| 02_gibbs-sampling-from-10-000-feet.mp4 | 18.4 MB |
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| 03_putting-it-together.mp4 | 18.4 MB |
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| 02_loading-exploring-house-sale-data.mp4 | 18.4 MB |
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| 03_visualizing-the-lasso-cost-and-solution.mp4 | 18.4 MB |
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| 06_running-gradient-descent.mp4 | 18.4 MB |
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| 04_approach-2-gradient-descent.mp4 | 18.4 MB |
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| 04_computing-exploring-tf-idfs.mp4 | 18.3 MB |
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| 04_data-likelihood.mp4 | 18.3 MB |
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| 05_goal-of-lda-inference.mp4 | 18.2 MB |
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| 01_balancing-fit-and-magnitude-of-coefficients.mp4 | 18.1 MB |
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| 04_very-optional-rewriting-the-log-likelihood-into-a-simpler-form.mp4 | 18.0 MB |
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| 03_creating-variables-in-python.mp4 | 17.7 MB |
| 256 | 321.1 KB |
| 01_slides-presented-in-this-module_mixmodel-EM-annotated.pdf | 17.7 MB |
| 257 | 325.3 KB |
| 02_loading-exploring-wikipedia-data.mp4 | 17.5 MB |
| 258 | 468.8 KB |
| 03_ensemble-classifiers.mp4 | 17.5 MB |
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| 02_error-analysis.mp4 | 17.5 MB |
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| 05_when-to-use-decision-trees.mp4 | 17.5 MB |
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| 02_the-cost-of-using-a-given-line.mp4 | 17.4 MB |
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| 04_k-nn-algorithm.mp4 | 17.4 MB |
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| 05_visualizing-the-cost-function.mp4 | 17.3 MB |
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| 01_threshold-splits-for-continuous-inputs.mp4 | 17.3 MB |
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| 02_vectorization-part-1.mp4 | 17.3 MB |
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| 03_vectorization-part-2.mp4 | 17.3 MB |
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| 01_the-k-means-algorithm.mp4 | 17.2 MB |
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| 04_discovering-hidden-structure-by-matrix-factorization.mp4 | 17.2 MB |
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| 03_scaling-mixtures-of-gaussians-for-document-clustering.mp4 | 17.2 MB |
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| 06_visualization-examples.mp4 | 17.2 MB |
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| 03_cost-function-with-regularization.mp4 | 17.1 MB |
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| 03_using-random-lines-to-partition-points.mp4 | 17.1 MB |
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| 02_more-complex-neural-networks.mp4 | 17.0 MB |
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| 01_computing-the-gradient-of-rss.mp4 | 17.0 MB |
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| 01_the-need-for-bayesian-inference.mp4 | 17.0 MB |
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| 02_k-fold-cross-validation.mp4 | 17.0 MB |
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| 04_effect-of-coefficient-values-on-predicted-probabilities.mp4 | 17.0 MB |
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| 04_learning-rate.mp4 | 16.9 MB |
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| 04_overfitting-demo.mp4 | 16.9 MB |
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| 03_false-positives-false-negatives-and-confusion-matrices.mp4 | 16.9 MB |
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| 02_gradient-descent-multidimensional-hill-descent.mp4 | 16.9 MB |
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| 06_choosing-the-number-of-clusters.mp4 | 16.9 MB |
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| 02_finding-maxima-or-minima-analytically.mp4 | 16.8 MB |
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| 03_overfitting-in-decision-trees.mp4 | 16.8 MB |
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| 01_inference-in-code.mp4 | 16.8 MB |
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| 03_cost-function-formula.mp4 | 16.7 MB |
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| 02_optional-pruning-decision-trees-to-avoid-overfitting.mp4 | 16.7 MB |
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| 03_a-worked-example-for-lda-deriving-the-resampling-distribution.mp4 | 16.7 MB |
| 289 | 1.3 MB |
| 01_em-iterates-in-equations-and-pictures.mp4 | 16.7 MB |
| 290 | 1.3 MB |
| 03_what-is-a-visual-product-recommender.mp4 | 16.7 MB |
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| 03_using-the-fitted-line.mp4 | 16.6 MB |
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| 03_finding-related-items.mp4 | 16.6 MB |
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| 03_an-alternative-document-clustering-model.mp4 | 16.6 MB |
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| 01_what-we-covered-and-what-we-didn-t-cover.mp4 | 16.5 MB |
| 295 | 1.5 MB |
| 01_a-performance-metric-for-recommender-systems.mp4 | 16.5 MB |
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| 03_an-unsupervised-task.mp4 | 16.5 MB |
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| 04_creating-evaluating-a-personalized-song-recommender.mp4 | 16.4 MB |
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| 05_gradient-descent-for-linear-regression.mp4 | 16.4 MB |
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| 05_full-cycle-of-a-machine-learning-project.mp4 | 16.4 MB |
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| 03_precision-recall-curves.mp4 | 16.3 MB |
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| 03_nn-search-with-kd-trees.mp4 | 16.3 MB |
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| 02_linear-regression-model-part-2.mp4 | 16.2 MB |
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| 01_ensemble-methods-impact-of-boosting-quick-recap.mp4 | 16.2 MB |
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| 06_outlining-the-second-half-of-the-course.mp4 | 16.2 MB |
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| 03_matrix-multiplication-rules.mp4 | 16.1 MB |
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| 02_feature-split-selection-with-missing-data.mp4 | 16.1 MB |
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| 06_decision-boundaries.mp4 | 16.0 MB |
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| 01_measuring-purity.mp4 | 16.0 MB |
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| 02_matrix-multiplication.mp4 | 15.9 MB |
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| 05_continuous-valued-features.mp4 | 15.9 MB |
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| 04_strategy-2-purification-by-imputing-missing-data.mp4 | 15.9 MB |
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| 02_whats-a-good-accuracy.mp4 | 15.8 MB |
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| 09_exploring-the-most-positive-negative-aspects-of-a-product.mp4 | 15.8 MB |
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| 02_addressing-overfitting.mp4 | 15.7 MB |
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| 04_finding-the-max-via-hill-climbing.mp4 | 15.7 MB |
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| 03_precision-recall-curve.mp4 | 15.7 MB |
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| 01_advanced-optimization.mp4 | 15.6 MB |
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| 01_precision-fraction-of-positive-predictions-that-are-actually-positive.mp4 | 15.6 MB |
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| 03_the-lasso-objective-and-its-coefficient-path.mp4 | 15.5 MB |
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| 01_regression-ml-block-diagram.mp4 | 15.5 MB |
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| 01_review-of-gradient-ascent.mp4 | 15.5 MB |
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| 02_rewriting-the-single-observation-model-in-vector-notation.mp4 | 15.4 MB |
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| 01_evaluating-overfitting-via-training-test-split.mp4 | 15.4 MB |
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| 05_computing-distances-between-wikipedia-articles.mp4 | 15.4 MB |
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| 04_the-dendrogram.mp4 | 15.3 MB |
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| 01_collaborative-filtering-people-who-bought-this-also-bought.mp4 | 15.3 MB |
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| 04_using-the-output-of-collapsed-gibbs-sampling.mp4 | 15.2 MB |
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| 04_interpreting-the-fitted-line.mp4 | 15.2 MB |
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| 03_selecting-best-feature-to-split-on.mp4 | 15.1 MB |
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| 02_coordinate-descent.mp4 | 15.1 MB |
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| 03_deploying-an-ml-service.mp4 | 15.1 MB |
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| 04_improved-implementation-of-softmax.mp4 | 15.1 MB |
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| 03_neural-network-with-softmax-output.mp4 | 15.0 MB |
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| 03_training-evaluating-a-classifier-using-raw-image-pixels.mp4 | 15.0 MB |
| 339 | 1.0 MB |
| 05_outlining-the-first-half-of-the-course.mp4 | 14.8 MB |
| 340 | 1.2 MB |
| 01_penalizing-large-coefficients-to-mitigate-overfitting.mp4 | 14.8 MB |
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| 01_iterative-loop-of-ml-development.mp4 | 14.8 MB |
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| 02_loading-and-exploring-song-data.mp4 | 14.8 MB |
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| 01_computing-the-gradient-of-the-ridge-objective.mp4 | 14.8 MB |
| 344 | 1.2 MB |
| 01_the-matrix-completion-task.mp4 | 14.8 MB |
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| 01_decision-tree-model.mp4 | 14.8 MB |
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| 02_visualizing-the-ridge-solution.mp4 | 14.7 MB |
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| 06_choosing-stepsize-and-convergence-criteria.mp4 | 14.7 MB |
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| 03_error-vs-amount-of-data.mp4 | 14.7 MB |
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| 02_state-action-value-function-example.mp4 | 14.6 MB |
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| 04_example-recognizing-images.mp4 | 14.6 MB |
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| 02_overfitting-in-boosting.mp4 | 14.6 MB |
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| 01_encoding-categorical-inputs.mp4 | 14.6 MB |
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| 06_calculating-tf-idf-vectors.mp4 | 14.5 MB |
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| 01_principle-of-occams-razor-learning-simpler-decision-trees.mp4 | 14.5 MB |
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| 03_mapreduce-execution-overview-and-combiners.mp4 | 14.4 MB |
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| 02_optional-responsibilities-as-bayes-rule.mp4 | 14.4 MB |
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| 01_modifying-decision-trees-to-handle-missing-data.mp4 | 14.4 MB |
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| 01_choosing-step-size.mp4 | 14.4 MB |
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| 03_supervised-learning-part-2.mp4 | 14.4 MB |
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| 02_using-stochastic-gradient-for-online-learning.mp4 | 14.4 MB |
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| 02_feature-scaling-part-2.mp4 | 14.4 MB |
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| 02_interpreting-the-mixture-of-gaussian-terms.mp4 | 14.4 MB |
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| 02_who-is-this-specialization-for.mp4 | 14.4 MB |
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| 02_sampling-with-replacement.mp4 | 14.3 MB |
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| 04_overfitting-in-classification.mp4 | 14.2 MB |
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| 02_distance-metrics.mp4 | 14.2 MB |
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| 02_recommendations-from-known-user-item-features.mp4 | 14.2 MB |
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| 04_using-one-hot-encoding-of-categorical-features.mp4 | 14.2 MB |
| 369 | 1.8 MB |
| 01_motivating-the-use-of-multiple-inputs.mp4 | 14.2 MB |
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| 01_overfitting-in-classifiers-leads-to-overconfident-predictions.mp4 | 14.1 MB |
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| 01_document-representation.mp4 | 14.1 MB |
| 372 | 1.9 MB |
| 02_approach-1-closed-form-solution.mp4 | 14.1 MB |
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| 01_slides-presented-in-this-module_clustering-intro-annotated.pdf | 14.1 MB |
| 374 | 1.9 MB |
| 01_gradients-derivatives-in-multiple-dimensions.mp4 | 14.0 MB |
| 375 | 2.0 MB |
| 02_learning-boosted-decision-stumps-with-adaboost.mp4 | 14.0 MB |
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| 01_summary-and-thank-you.mp4 | 13.9 MB |
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| 02_supervised-learning-part-1.mp4 | 13.9 MB |
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| 02_thresholding-ridge-coefficients.mp4 | 13.9 MB |
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| 03_what-do-we-mean-by-loss.mp4 | 13.9 MB |
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| 03_weighted-k-nearest-neighbors.mp4 | 13.8 MB |
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| 03_what-is-good-performance-for-a-classifier.mp4 | 13.8 MB |
| 382 | 194.4 KB |
| 01_implementing-em-for-gaussian-mixtures_images.zip | 13.8 MB |
| 383 | 218.2 KB |
| 02_starting-a-jupyter-notebook.mp4 | 13.8 MB |
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| 03_example-of-computing-derivative-for-logistic-regression.mp4 | 13.8 MB |
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| 02_predicting-loan-defaults-with-decision-trees.mp4 | 13.7 MB |
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| 04_interpreting-derivative-for-logistic-regression.mp4 | 13.7 MB |
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| 08_exploring-other-features-of-the-data.mp4 | 13.7 MB |
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| 03_all-subsets.mp4 | 13.7 MB |
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| 01_feature-scaling-part-1.mp4 | 13.6 MB |
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| 02_the-resulting-ridge-objective-and-its-extreme-solutions.mp4 | 13.6 MB |
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| 01_slides-presented-in-this-module_retrieval-intro-annotated.pdf | 13.6 MB |
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| 03_visualizing-effect-of-l2-regularization-in-logistic-regression.mp4 | 13.6 MB |
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| 04_complexity-of-nn-search-with-kd-trees.mp4 | 13.5 MB |
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| 05_prioritizing-important-words-with-tf-idf.mp4 | 13.5 MB |
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| 04_other-regression-examples.mp4 | 13.5 MB |
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| 03_discussing-the-closed-form-solution.mp4 | 13.5 MB |
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| 04_building-a-recommender-system-via-classification.mp4 | 13.4 MB |
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| 04_matrix-multiplication-code.mp4 | 13.4 MB |
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| 02_approach-1-closed-form-solution.mp4 | 13.3 MB |
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| 04_querying-the-nearest-neighbors-model-to-retrieve-images.mp4 | 13.3 MB |
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| 02_deep-learning-performance.mp4 | 13.2 MB |
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| 02_the-boosting-question.mp4 | 13.2 MB |
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| 02_mixed-membership-models-for-documents.mp4 | 13.2 MB |
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| 03_gradient-descent-intuition.mp4 | 13.2 MB |
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| 02_trading-off-precision-and-recall.mp4 | 13.2 MB |
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| 03_visualizing-decision-boundaries.mp4 | 13.2 MB |
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| 08_applying-model-to-find-most-positive-negative-reviews-for-a-product.mp4 | 13.2 MB |
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| 03_creating-evaluating-a-popularity-based-song-recommender.mp4 | 13.2 MB |
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| 02_weighted-error.mp4 | 13.2 MB |
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| 02_divisive-clustering.mp4 | 13.1 MB |
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| 02_simple-and-multiple-regression.mp4 | 13.0 MB |
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| 01_training-and-evaluating-a-classifier.mp4 | 13.0 MB |
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| 07_examples-of-document-retrieval-in-action.mp4 | 12.9 MB |
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| 05_tensorflow-implementation-of-content-based-filtering.mp4 | 12.9 MB |
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| 03_test-error-what-we-can-actually-compute.mp4 | 12.9 MB |
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| 03_why-do-we-need-activation-functions.mp4 | 12.9 MB |
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| 03_strategy-1-purification-by-skipping-missing-data.mp4 | 12.9 MB |
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| 02_learning-a-decision-stump.mp4 | 12.9 MB |
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| 01_very-optional-deriving-gradient-of-logistic-regression-log-trick.mp4 | 12.9 MB |
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| 02_lsh-as-an-alternative-to-kd-trees.mp4 | 12.9 MB |
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| 05_the-future-of-intelligent-applications.mp4 | 12.8 MB |
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| 01_gradient-descent-implementation.mp4 | 12.8 MB |
| 423 | 1.2 MB |
| 01_implementing-logistic-regression-from-scratch_amazon_baby_subset.sframe.zip | 12.7 MB |
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| 01_logistic-regression-with-l2-regularization_amazon_baby_subset.sframe.zip | 12.7 MB |
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| 01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.sframe.zip | 12.7 MB |
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| 01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.gl.zip | 12.7 MB |
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| 01_implementing-logistic-regression-from-scratch_amazon_baby_subset.gl.zip | 12.7 MB |
| 428 | 1.3 MB |
| 01_logistic-regression-with-l2-regularization_amazon_baby_subset.gl.zip | 12.7 MB |
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| 03_welcome.mp4 | 4.9 MB |
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| 01_recap-of-overfitting-and-regularization-in-decision-trees.mp4 | 4.9 MB |
| 658 | 1.1 MB |
| 01_making-predictions-with-decision-trees.mp4 | 4.8 MB |
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| 03_creating-a-nearest-neighbors-model-for-image-retrieval.mp4 | 4.7 MB |
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| 04_a-brief-recap.mp4 | 4.7 MB |
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| 01_predicting-house-prices-assignment_house_images.zip | 4.7 MB |
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| 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_house_images.zip | 4.7 MB |
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| 05_querying-for-the-most-similar-images-for-car-image.mp4 | 4.6 MB |
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| 03_welcome-to-the-classification-course-a-part-of-the-machine-learning.mp4 | 4.6 MB |
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| 02_recommender-systems-overview.mp4 | 4.6 MB |
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| 01_slides-presented-in-this-module_week6_NNkernelregression-annotated.pdf | 4.5 MB |
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| 06_a-brief-recap.mp4 | 4.5 MB |
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| 03_what-you-ll-be-able-to-do.mp4 | 4.4 MB |
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| 01_a-brief-recap.mp4 | 4.4 MB |
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| 01_slides-presented-in-this-module_closing.pdf | 4.3 MB |
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| 05_impact-of-classification.mp4 | 4.0 MB |
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| 01_a-brief-recap.mp4 | 3.9 MB |
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| 01_recap-of-overfitting-regularization-in-logistic-regression.mp4 | 3.8 MB |
| 674 | 170.1 KB |
| 02_predicting-house-prices-a-case-study-in-regression.mp4 | 3.8 MB |
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| 02_a-case-study-in-predicting-house-prices.mp4 | 3.8 MB |
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| 02_a-brief-recap.mp4 | 3.7 MB |
| 677 | 271.6 KB |
| 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki_map_index_to_word.gl.zip | 3.7 MB |
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| 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_map_index_to_word.gl.zip | 3.7 MB |
| 679 | 310.2 KB |
| 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_map_index_to_word.gl.zip | 3.7 MB |
| 680 | 310.2 KB |
| 01_clustering-text-data-with-k-means_people_wiki_map_index_to_word.gl.zip | 3.7 MB |
| 681 | 310.2 KB |
| 01_slides-presented-in-this-module_online-learning-annotated.pdf | 3.6 MB |
| 682 | 358.5 KB |
| 01_recap-of-decision-trees.mp4 | 3.4 MB |
| 683 | 583.4 KB |
| 07_inspecting-the-model-coefficients-learned.mp4 | 3.4 MB |
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| 03_welcome-to-this-course-and-specialization.mp4 | 3.3 MB |
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| 01_clustering-text-data-with-gaussian-mixtures_4_tf_idf.npz.zip | 3.2 MB |
| 686 | 775.4 KB |
| 01_slides-presented-in-this-module_decision-trees-overfitting-annotated.pdf | 3.1 MB |
| 687 | 921.5 KB |
| 01_slides-presented-in-this-module_week4_ridgeregression-annotated.pdf | 3.0 MB |
| 688 | 1014.2 KB |
| 01_slides-presented-in-this-module_logistic-regression-learning-annotated.pdf | 3.0 MB |
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| 02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.mp4 | 2.9 MB |
| 690 | 1.1 MB |
| 01_slides-presented-in-this-module_decision-trees-annotated.pdf | 2.8 MB |
| 691 | 1.2 MB |
| 02_you-ve-made-it.mp4 | 2.8 MB |
| 692 | 1.2 MB |
| 05_lets-get-started.mp4 | 2.8 MB |
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| 01_slides-presented-in-this-module_logistic-regression-overfitting-annotated.pdf | 2.6 MB |
| 694 | 1.4 MB |
| 01_predicting-house-prices-assignment_home_data.csv | 2.5 MB |
| 695 | 1.5 MB |
| 01_slides-presented-in-this-module_boosting-annotated.pdf | 2.2 MB |
| 696 | 1.8 MB |
| 02_document-retrieval-a-case-study-in-clustering-and-measuring-similarity.mp4 | 2.1 MB |
| 697 | 1.9 MB |
| 01_slides-presented-in-this-module_mikolajczyk_pami05.pdf | 2.0 MB |
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