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Total Size |
10.9 GB |
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Total Files |
236 |
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Hash |
8D94D5B14AF768DCC0370B3CB61CC7688DABBC57 |
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/.../004-machine-learning-why-should-you-jump-on-the-bandwagon/ |
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791.2 KB |
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/.../005-plunging-in-machine-learning-approaches-to-spam-detection/ |
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775.2 KB |
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/.../006-spam-detection-with-machine-learning-continued/ |
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892.7 KB |
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/.../007-get-the-lay-of-the-land-types-of-machine-learning-problems/ |
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2.2 MB |
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/.../009-random-variables/ |
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973.0 KB |
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/.../010-bayes-theorem/ |
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944.5 KB |
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/.../011-naive-bayes-classifier/ |
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1.4 MB |
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/.../012-naive-bayes-classifier-an-example/ |
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1.9 MB |
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/.../013-knearest-neighbors/ |
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835.5 KB |
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/.../014-knearest-neighbors-a-few-wrinkles/ |
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924.4 KB |
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/.../015-support-vector-machines-introduced/ |
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856.8 KB |
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/.../016-support-vector-machines-maximum-margin-hyperplane-and-kernel-trick/ |
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2.1 MB |
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/.../017-artificial-neural-networksperceptrons-introduced/ |
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1.5 MB |
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868.2 KB |
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/.../018-clustering-introduction/ |
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3.1 MB |
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973.9 KB |
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/.../019-clustering-kmeans-and-dbscan/ |
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3.1 MB |
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/.../020-association-rules-learning/ |
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845.0 KB |
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/.../021-dimensionality-reduction/ |
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934.2 KB |
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/.../022-principal-component-analysis/ |
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902.1 KB |
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/.../023-regression-introduced-linear-and-logistic-regression/ |
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1.2 MB |
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/.../024-bias-variance-tradeoff/ |
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369.8 KB |
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/.../026-installing-python-anaconda-and-pip/ |
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90.7 MB |
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/.../027-natural-language-processing-with-nltk/ |
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3.5 KB |
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3.5 KB |
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/.../028-natural-language-processing-with-nltk-see-it-in-action/ |
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3.5 KB |
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3.5 KB |
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/.../029-web-scraping-with-beautifulsoup/ |
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4.5 KB |
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4.5 KB |
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/.../030-a-serious-nlp-application-text-auto-summarization-using-python/ |
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908.9 KB |
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/.../031-python-drill-autosummarize-news-articles-i/ |
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12.9 KB |
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12.9 KB |
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118.1 KB |
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/.../032-python-drill-autosummarize-news-articles-ii/ |
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12.9 KB |
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12.9 KB |
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/.../033-python-drill-autosummarize-news-articles-iii/ |
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12.9 KB |
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12.9 KB |
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/.../034-put-it-to-work-news-article-classification-using-knearest-neighbors/ |
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895.3 KB |
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1.1 MB |
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/.../035-put-it-to-work-news-article-classification-using-naive-bayes-classifier/ |
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769.6 KB |
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/.../036-python-drill-scraping-news-websites/ |
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17.0 KB |
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17.0 KB |
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/.../037-python-drill-feature-extraction-with-nltk/ |
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17.0 KB |
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17.0 KB |
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/.../038-python-drill-classification-with-knn/ |
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17.0 KB |
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17.0 KB |
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/.../039-python-drill-classification-with-naive-bayes/ |
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17.0 KB |
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17.0 KB |
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/.../040-document-distance-using-tfidf/ |
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1.2 MB |
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/.../041-put-it-to-work-news-article-clustering-with-kmeans-and-tfidf/ |
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961.5 KB |
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/.../042-python-drill-clustering-with-k-means/ |
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17.0 KB |
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17.0 KB |
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/.../044-sentiment-analysis-whats-all-the-fuss-about/ |
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3.0 MB |
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/.../045-ml-solutions-for-sentiment-analysis-the-devil-is-in-the-details/ |
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3.0 MB |
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/.../046-sentiment-lexicons-with-an-introduction-to-wordnet-and-sentiwordnet/ |
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3.0 MB |
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/.../047-regular-expressions/ |
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3.0 MB |
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/.../048-regular-expressions-in-python/ |
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3.0 MB |
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/.../049-put-it-to-work-twitter-sentiment-analysis/ |
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3.0 MB |
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/.../050-twitter-sentiment-analysis-work-the-api/ |
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17.4 KB |
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/.../051-twitter-sentiment-analysis-regular-expressions-for-preprocessing/ |
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17.4 KB |
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/.../052-twitter-sentiment-analysis-naive-bayes-svm-and-sentiwordnet/ |
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17.4 KB |
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/.../055-growing-the-tree-decision-tree-learning/ |
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19.6 MB |
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/.../056-branching-out-information-gain/ |
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19.6 MB |
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/.../057-decision-tree-algorithms/ |
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19.6 MB |
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/.../058-titanic-decision-trees-predict-survival-kaggle-i/ |
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12.6 KB |
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12.5 KB |
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/.../059-titanic-decision-trees-predict-survival-kaggle-ii/ |
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12.6 KB |
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12.5 KB |
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/.../060-titanic-decision-trees-predict-survival-kaggle-iii/ |
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12.6 KB |
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12.5 KB |
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/.../061-overfitting-the-bane-of-machine-learning/ |
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3.0 MB |
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/.../062-overfitting-continued/ |
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3.0 MB |
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/.../063-cross-validation/ |
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3.0 MB |
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/.../064-simplicity-is-a-virtue-regularization/ |
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3.0 MB |
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/.../065-the-wisdom-of-crowds-ensemble-learning/ |
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3.0 MB |
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/.../066-ensemble-learning-continued-bagging-boosting-and-stacking/ |
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3.0 MB |
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/.../067-random-forests-much-more-than-trees/ |
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3.0 MB |
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/.../068-back-on-the-titanic-cross-validation-and-random-forests/ |
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17.9 KB |
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17.9 KB |
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/.../070-what-do-amazon-and-netflix-have-in-common/ |
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29.6 MB |
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/.../071-recommendation-engines-a-look-inside/ |
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29.6 MB |
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/.../072-what-are-you-made-of-contentbased-filtering/ |
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29.6 MB |
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/.../073-with-a-little-help-from-friends-collaborative-filtering/ |
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29.6 MB |
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/.../074-a-neighbourhood-model-for-collaborative-filtering/ |
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29.6 MB |
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/.../075-top-picks-for-you-recommendations-with-neighbourhood-models/ |
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29.6 MB |
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/.../076-discover-the-underlying-truth-latent-factor-collaborative-filtering/ |
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29.6 MB |
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/.../077-latent-factor-collaborative-filtering-contd/ |
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29.6 MB |
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/.../078-gray-sheep-and-shillings-challenges-with-collaborative-filtering/ |
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29.6 MB |
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/.../079-the-apriori-algorithm-for-association-rules/ |
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29.6 MB |
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/.../080-back-to-basics-numpy-in-python/ |
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7.2 KB |
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7.2 KB |
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/.../081-back-to-basics-numpy-and-scipy-in-python/ |
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7.2 KB |
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7.2 KB |
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/.../082-movielens-and-pandas/ |
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18.8 KB |
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18.8 KB |
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/.../083-code-along-whats-my-favorite-movie-data-analysis-with-pandas/ |
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18.8 KB |
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18.8 KB |
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/.../084-code-along-movie-recommendation-with-nearest-neighbour-cf/ |
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18.8 KB |
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18.8 KB |
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/.../085-code-along-top-movie-picks-nearest-neighbour-cf/ |
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18.8 KB |
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18.8 KB |
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/.../086-code-along-movie-recommendations-with-matrix-factorization/ |
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18.8 KB |
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18.8 KB |
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/.../087-code-along-association-rules-with-the-apriori-algorithm/ |
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18.8 KB |
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18.8 KB |
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/.../088-computer-vision-an-introduction/ |
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9.5 MB |
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/.../089-perceptron-revisited/ |
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9.5 MB |
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/.../090-deep-learning-networks-introduced/ |
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9.5 MB |
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/.../091-code-along-handwritten-digit-recognition-i/ |
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10.4 KB |
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10.6 KB |
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/.../092-code-along-handwritten-digit-recognition-ii/ |
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10.4 KB |
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10.6 KB |
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/.../093-code-along-handwritten-digit-recognition-iii/ |
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10.4 KB |
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10.6 KB |
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/16-quizzes/quizzes/ |
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2.6 KB |
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/01-introduction/ |
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16.1 MB |
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28.6 MB |
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/02-jump-right-in-machine-learning-for-spam-detection/ |
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14.5 MB |
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004-machine-learning-why-should-you-jump-on-the-bandwagon.mp4 |
22.6 MB |
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005-plunging-in-machine-learning-approaches-to-spam-detection.mp4 |
33.8 MB |
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30.5 MB |
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007-get-the-lay-of-the-land-types-of-machine-learning-problems.mp4 |
31.5 MB |
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/03-solving-classification-problems/ |
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6.2 MB |
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37.1 MB |
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26.0 MB |
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16.8 MB |
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29.8 MB |
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117.2 MB |
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161.7 MB |
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96.4 MB |
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016-support-vector-machines-maximum-margin-hyperplane-and-kernel-trick.mp4 |
181.9 MB |
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124.2 MB |
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/04-clustering-as-a-form-of-unsupervised-learning/ |
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198.4 MB |
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148.5 MB |
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/05-association-detection/ |
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99.6 MB |
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/06-dimensionality-reduction/ |
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103.3 MB |
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181.0 MB |
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/07-regression-as-a-form-of-supervised-learning/ |
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023-regression-introduced-linear-and-logistic-regression.mp4 |
147.6 MB |
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116.1 MB |
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/08-natural-language-processing-and-python/ |
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6.5 MB |
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41.1 MB |
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79.9 MB |
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028-natural-language-processing-with-nltk-see-it-in-action.mp4 |
124.0 MB |
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158.1 MB |
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030-a-serious-nlp-application-text-auto-summarization-using-python.mp4 |
127.2 MB |
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113.5 MB |
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126.6 MB |
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208.0 MB |
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034-put-it-to-work-news-article-classification-using-knearest-neighbors.mp4 |
218.1 MB |
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035-put-it-to-work-news-article-classification-using-naive-bayes-classifier.mp4 |
212.1 MB |
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139.7 MB |
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120.8 MB |
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32.3 MB |
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58.6 MB |
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111.9 MB |
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041-put-it-to-work-news-article-clustering-with-kmeans-and-tfidf.mp4 |
148.2 MB |
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57.0 MB |
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/09-sentiment-analysis/ |
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56.8 MB |
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163.6 MB |
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045-ml-solutions-for-sentiment-analysis-the-devil-is-in-the-details.mp4 |
182.0 MB |
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046-sentiment-lexicons-with-an-introduction-to-wordnet-and-sentiwordnet.mp4 |
163.2 MB |
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161.0 MB |
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48.3 MB |
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156.1 MB |
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216.2 MB |
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051-twitter-sentiment-analysis-regular-expressions-for-preprocessing.mp4 |
114.8 MB |
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052-twitter-sentiment-analysis-naive-bayes-svm-and-sentiwordnet.mp4 |
191.8 MB |
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/10-decision-trees/ |
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6.5 MB |
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143.4 MB |
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168.6 MB |
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212.1 MB |
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68.8 MB |
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119.7 MB |
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100.8 MB |
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79.4 MB |
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/11-a-few-useful-things-to-know-about-overfitting/ |
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159.0 MB |
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102.4 MB |
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152.0 MB |
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50.3 MB |
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149.7 MB |
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066-ensemble-learning-continued-bagging-boosting-and-stacking.mp4 |
191.0 MB |
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/12-random-forests/ |
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108.8 MB |
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068-back-on-the-titanic-cross-validation-and-random-forests.mp4 |
210.6 MB |
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/13-recommendation-systems/ |
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6.8 MB |
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177.9 MB |
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103.0 MB |
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112.1 MB |
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073-with-a-little-help-from-friends-collaborative-filtering.mp4 |
101.5 MB |
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166.4 MB |
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075-top-picks-for-you-recommendations-with-neighbourhood-models.mp4 |
101.8 MB |
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076-discover-the-underlying-truth-latent-factor-collaborative-filtering.mp4 |
207.1 MB |
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148.4 MB |
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078-gray-sheep-and-shillings-challenges-with-collaborative-filtering.mp4 |
75.1 MB |
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189.9 MB |
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/14-recommendation-systems-in-python/ |
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128.6 MB |
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88.9 MB |
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134.6 MB |
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083-code-along-whats-my-favorite-movie-data-analysis-with-pandas.mp4 |
27.7 MB |
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084-code-along-movie-recommendation-with-nearest-neighbour-cf.mp4 |
166.8 MB |
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58.8 MB |
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086-code-along-movie-recommendations-with-matrix-factorization.mp4 |
174.0 MB |
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087-code-along-association-rules-with-the-apriori-algorithm.mp4 |
50.9 MB |
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/15-a-taste-of-deep-learning-and-computer-vision/ |
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122.8 MB |
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107.8 MB |
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106.7 MB |
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106.9 MB |
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119.3 MB |
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35.9 MB |
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Total files 236 |
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