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Applied Data Science with Python Specialization
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[TutsNode.net] - Applied Data Science with Python Specialization (Size: 3.50 GB) (Files: 745)
[TutsNode.net] - Applied Data Science with Python Specialization
python-social-network-analysis
04_network-evolution
01_module-4-applications
03_small-world-networks.mp4
04_link-prediction.mp4
02_power-laws-and-rich-get-richer-phenomena-optional_networks-book-ch18.pdf
01_preferential-attachment-model.mp4
05_module-4-quiz_exam.html
03_small-world-networks.en.srt
04_link-prediction.en.srt
03_small-world-networks.en.txt
01_preferential-attachment-model.en.srt
04_link-prediction.en.txt
01_preferential-attachment-model.en.txt
02_power-laws-and-rich-get-richer-phenomena-optional_instructions.html
06_the-small-world-phenomenon-optional_instructions.html
06_the-small-world-phenomenon-optional_networks-book-ch02.pdf
06_the-small-world-phenomenon-optional_networks-book-ch20.pdf
03_post-course-survey
02_keep-learning-with-michigan-online_instructions.html
01_post-course-survey_instructions.html
02_module-4-assignment
01_assignment-4-submission_instructions.html
01_why-study-networks-and-basics-on-networkx
01_module-1-why-study-networks-and-basics-on-networkx
04_network-definition-and-vocabulary.en.txt
06_bipartite-graphs.mp4
05_node-and-edge-attributes.en.srt
09_module-1-quiz_exam.html
04_network-definition-and-vocabulary.mp4
06_bipartite-graphs.en.srt
01_syllabus_instructions.html
04_network-definition-and-vocabulary.en.srt
06_bipartite-graphs.en.txt
03_networks-definition-and-why-we-study-them.en.srt
08_ta-demonstration-loading-graphs-in-networkx.en.srt
02_help-us-learn-more-about-you_instructions.html
07_notice-for-auditing-learners-assignment-submission_instru
05_node-and-edge-attributes.en.txt
03_networks-definition-and-why-we-study-them.en.txt
08_ta-demonstration-loading-graphs-in-networkx.en.txt
03_networks-definition-and-why-we-study-them.mp4
05_node-and-edge-attributes.mp4
08_ta-demonstration-loading-graphs-in-networkx.mp4
02_module-1-assignment
01_assignment-1-submission_instructions.html
05_Resources
01_jupyter-notebook-faq
01__resources.html
02_additional-resources
01__documentation.html
01__Scikit_Learn_Cheat_Sheet_Python.pdf
01__classes.html
01__resources.html
03_code-sharing-policy
01__resources.html
04_lecture-slides
01__resources.html
01__3.5_Hubs_and_Authorities.pdf
01__1.1_Networks_Everywhere.pdf
01__3.3_Basic_Page_Rank.pdf
01__2.4_Network_Robustness.pdf
01__3.6_Centrality_Examples.pdf
01__4.3_Link_Prediction.pdf
01__4.2_Small_World_Networks.pdf
01__4.1_Preferential_Attachment_Model.pdf
01__2.3_Connected_Components.pdf
01__3.4_Scaled_Page_Rank.pdf
01__3.2_Betweenness_Centrality.pdf
01__1.2_Network_Definition_and_Vocabulary.pdf
01__2.1_Clustering_Coefficient.pdf
01__2.2_Distance_Measures.pdf
01__3.1_Degree_and_Closeness_Centrality.pdf
01__1.4_Bipartite_Graphs.pdf
01__1.3_Node_and_Edge_Attributes.pdf
05_attributions-credits
01__resources.html
03_influence-measures-and-network-centralization
01_module-3-influence-measures-and-network-centralization
05_hubs-and-authorities.mp4
07_module-3-quiz_exam.html
04_scaled-page-rank.mp4
02_betweenness-centrality.mp4
03_basic-page-rank.en.srt
01_degree-and-closeness-centrality.mp4
06_centrality-examples.mp4
02_betweenness-centrality.en.srt
01_degree-and-closeness-centrality.en.srt
05_hubs-and-authorities.en.srt
02_betweenness-centrality.en.txt
06_centrality-examples.en.srt
03_basic-page-rank.mp4
04_scaled-page-rank.en.srt
05_hubs-and-authorities.en.txt
01_degree-and-closeness-centrality.en.txt
03_basic-page-rank.en.txt
04_scaled-page-rank.en.txt
06_centrality-examples.en.txt
02_module-3-assignment
01_assignment-3-submission_instructions.html
02_network-connectivity
01_module-2-network-connectivity
02_distance-measures.mp4
04_network-robustness.mp4
02_distance-measures.en.srt
01_clustering-coefficient.en.srt
02_distance-measures.en.txt
04_network-robustness.en.srt
03_connected-components.en.srt
01_clustering-coefficient.en.txt
04_network-robustness.en.txt
03_connected-components.en.txt
05_ta-demonstration-simple-network-visualizations-in-networkx.en.srt
05_ta-demonstration-simple-network-visualizations-in-networkx.en.txt
01_clustering-coefficient.mp4
03_connected-components.mp4
05_ta-demonstration-simple-network-visualizations-in-networkx.mp4
06_module-2-quiz_exam.html
02_module-2-assignment
01_assignment-2-submission_instructions.html
python-text-mining
05_Resources
04_lecture-slides
01__4.1_Semantic_Text_Similarity.pdf
01__2.3_Advanced_NLP_Tasks_with_NLTK.pdf
01__1.2_Handling_Text_in_Python.pdf
01__4.3_Generative_Models_and_LDA.pdf
01__2.1_Basic_Natural_Language_Processing.pdf
01__1.4_Internationalization_and_Issues_with_Non-ASCII_Characters.pdf
01__3.1_Text_Classification.pdf
01__3.3_Naive_Bayes_Classifier.pdf
01__3.5_Support_Vector_Machines.pdf
01__4.4_Information_Extraction.pdf
01__1.3_Regular_Expressions.pdf
01__4.2_Topic_Modeling.pdf
01__2.2_Basic_NLP_Tasks_with_NLTK.pdf
01__3.6_Learning_Text_Classifiers_in_Python.pdf
01__3.4_Naive_Bayes_Variations.pdf
01__3.2_Identifying_Features_from_Text.pdf
01__resources.html
01__1.1_Introduction_to_Text_Mining.pdf
01_jupyter-notebook-faq
01__resources.html
02_additional-resources
01__Scikit_Learn_Cheat_Sheet_Python.pdf
01__intro.html
01__documentation.html
01__classes.html
01__resources.html
03_code-sharing-policy
01__resources.html
05_attributions-credits
01__resources.html
03_module-3-classification-of-text
01_module-3-classification-of-text
07_demonstration-case-study-sentiment-analysis.en.txt
05_support-vector-machines.en.srt
05_support-vector-machines.mp4
03_naive-bayes-classifiers.mp4
02_identifying-features-from-text.en.srt
02_identifying-features-from-text.mp4
03_naive-bayes-classifiers.en.srt
06_learning-text-classifiers-in-python.en.srt
05_support-vector-machines.en.txt
01_text-classification.en.srt
03_naive-bayes-classifiers.en.txt
07_demonstration-case-study-sentiment-analysis.en.srt
06_learning-text-classifiers-in-python.en.txt
01_text-classification.en.txt
08_module-3-quiz_exam.html
04_naive-bayes-variations.en.srt
02_identifying-features-from-text.en.txt
04_naive-bayes-variations.en.txt
01_text-classification.mp4
06_learning-text-classifiers-in-python.mp4
07_demonstration-case-study-sentiment-analysis.mp4
04_naive-bayes-variations.mp4
02_assignment-3
01_assignment-3-submission_instructions.html
04_module-4-topic-modeling
01_module-4-topic-modeling
06_additional-resources-readings_wordnet.html
03_generative-models-and-lda.mp4
05_information-extraction.mp4
06_additional-resources-readings_blei03a.pdf
01_semantic-text-similarity.en.txt
05_information-extraction.en.srt
01_semantic-text-similarity.en.srt
04_practice-quiz_quiz.html
03_generative-models-and-lda.en.srt
05_information-extraction.en.txt
03_generative-models-and-lda.en.txt
02_topic-modeling.en.srt
02_topic-modeling.en.txt
07_module-4-quiz_exam.html
06_additional-resources-readings_instructions.html
01_semantic-text-similarity.mp4
02_topic-modeling.mp4
03_post-course-survey
02_keep-learning-with-michigan-online_instructions.html
01_post-course-survey_instructions.html
02_assignment-4
01_assignment-4-submission_instructions.html
02_module-2-basic-natural-language-processing
01_module-2-basic-natural-language-processing
02_basic-nlp-tasks-with-nltk.en.srt
02_basic-nlp-tasks-with-nltk.mp4
03_advanced-nlp-tasks-with-nltk.en.srt
02_basic-nlp-tasks-with-nltk.en.txt
03_advanced-nlp-tasks-with-nltk.en.txt
05_module-2-quiz_exam.html
01_basic-natural-language-processing.en.srt
01_basic-natural-language-processing.en.txt
04_practice-quiz_quiz.html
03_advanced-nlp-tasks-with-nltk.mp4
01_basic-natural-language-processing.mp4
02_assignment-2
01_assignment-2-submission_instructions.html
01_module-1-working-with-text-in-python
01_module-1-working-with-text-in-python
04_handling-text-in-python.en.srt
10_resources-common-issues-with-free-text_re.html
06_regular-expressions.en.srt
09_internationalization-and-issues-with-non-ascii-characters.en.srt
04_handling-text-in-python.en.txt
06_regular-expressions.en.txt
02_syllabus_instructions.html
11_module-1-quiz_exam.html
09_internationalization-and-issues-with-non-ascii-characters.en.txt
08_practice-quiz_quiz.html
07_demonstration-regex-with-pandas-and-named-groups.en.srt
01_introduction-to-text-mining.en.srt
07_demonstration-regex-with-pandas-and-named-groups.en.txt
01_introduction-to-text-mining.en.txt
03_help-us-learn-more-about-you_instructions.html
05_notice-for-auditing-learners-assignment-submission_instructions.html
10_resources-common-issues-with-free-text_instructions.html
06_regular-expressions.mp4
04_handling-text-in-python.mp4
09_internationalization-and-issues-with-non-ascii-characters.mp4
07_demonstration-regex-with-pandas-and-named-groups.mp4
01_introduction-to-text-mining.mp4
02_assignment-1
01_assignment-1-submission_instructions.html
python-machine-learning
04_module-4-supervised-machine-learning-part-2
01_module-4-supervised-machine-learning-part-2
12_leakage-in-data-mining-formulation-detection-and-avoidance-optional_c
02_random-forests.en.txt
14_rules-of-machine-learning-best-practices-for-ml-engineering-optional_
03_gradient-boosted-decision-trees.en.txt
04_neural-networks.mp4
05_neural-networks-made-easy-optional_instructions.html
09_assisting-pathologists-in-detecting-cancer-with-deep-learning-optiona
04_neural-networks.en.srt
06_play-with-neural-networks-tensorflow-playground-optional_instructions
08_deep-learning-in-a-nutshell-core-concepts-optional_instructions.html
11_the-treachery-of-leakage-optional_instructions.html
12_leakage-in-data-mining-formulation-detection-and-avoidance-optional_i
13_data-leakage-example-the-icml-2013-whale-challenge-optional_instructi
04_neural-networks.en.txt
02_random-forests.en.srt
10_data-leakage.en.srt
10_data-leakage.mp4
01_naive-bayes-classifiers.en.srt
10_data-leakage.en.txt
07_deep-learning-optional.en.srt
03_gradient-boosted-decision-trees.en.srt
01_naive-bayes-classifiers.en.txt
07_deep-learning-optional.en.txt
02_random-forests.mp4
01_naive-bayes-classifiers.mp4
07_deep-learning-optional.mp4
03_gradient-boosted-decision-trees.mp4
15_module-4-quiz_exam.html
04_conclusion
03_keep-learning-with-michigan-online_instructions.html
01_conclusion.en.txt
02_post-course-survey_instructions.html
01_conclusion.en.srt
01_conclusion.mp4
03_optional-unsupervised-machine-learning
03_clustering.en.srt
04_how-to-use-t-sne-effectively_instructions.html
05_how-machines-make-sense-of-big-data-an-introduction-to-clustering-algorith
02_dimensionality-reduction-and-manifold-learning.en.srt
03_clustering.en.txt
02_dimensionality-reduction-and-manifold-learning.en.txt
01_introduction.en.srt
01_introduction.en.txt
03_clustering.mp4
02_dimensionality-reduction-and-manifold-learning.mp4
01_introduction.mp4
02_assignment-4
01_assignment-4-submission_instructions.html
02_module-2-supervised-machine-learning-part-1
01_module-2-supervised-machine-learning
10_kernelized-support-vector-machines.en.srt
15_module-2-quiz_exam.html
06_linear-regression-ridge-lasso-and-polynomial-regression.mp4
06_linear-regression-ridge-lasso-and-polynomial-regression.en.srt
12_decision-trees.en.srt
10_kernelized-support-vector-machines.mp4
09_multi-class-classification.en.srt
01_introduction-to-supervised-machine-learning.mp4
12_decision-trees.mp4
07_logistic-regression.en.srt
13_a-few-useful-things-to-know-about-machine-learning_instructions.html
14_ed-yong-genetic-test-for-autism-refuted-optional_instructions.html
01_introduction-to-supervised-machine-learning.en.srt
05_linear-regression-least-squares.en.srt
12_decision-trees.en.txt
04_k-nearest-neighbors-classification-and-regression.en.srt
06_linear-regression-ridge-lasso-and-polynomial-regression.en.txt
10_kernelized-support-vector-machines.en.txt
02_overfitting-and-underfitting.en.srt
08_linear-classifiers-support-vector-machines.en.srt
01_introduction-to-supervised-machine-learning.en.txt
05_linear-regression-least-squares.en.txt
11_cross-validation.en.srt
07_logistic-regression.en.txt
04_k-nearest-neighbors-classification-and-regression.en.txt
02_overfitting-and-underfitting.en.txt
08_linear-classifiers-support-vector-machines.en.txt
11_cross-validation.en.txt
03_supervised-learning-datasets.en.srt
09_multi-class-classification.en.txt
03_supervised-learning-datasets.en.txt
05_linear-regression-least-squares.mp4
08_linear-classifiers-support-vector-machines.mp4
04_k-nearest-neighbors-classification-and-regression.mp4
07_logistic-regression.mp4
11_cross-validation.mp4
02_overfitting-and-underfitting.mp4
09_multi-class-classification.mp4
13_a-few-useful-things-to-know-about-machine-learning_cacm12.pdf
03_supervised-learning-datasets.mp4
02_assignment-2
01_assignment-2-submission_instructions.html
05_Resources
02_jupyter-notebook-faq
01__resources.html
01_additional-resources
01__documentation.html
01__Scikit_Learn_Cheat_Sheet_Python.pdf
01__classes.html
01__resources.html
03_course-slides
01__resources.html
01__02-adspy-module2-supervised1.pdf
01__01-adspy-module1-basics.pdf
01__05-adspy-unsupervised.pdf
01__04-adspy-module4-supervised2.pdf
01__03-adspy-module3-evaluation.pdf
04_code-sharing-policy
01__resources.html
05_attributions-credits
01__resources.html
03_module-3-evaluation
01_module-3-evaluation
01_model-evaluation-selection.mp4
02_confusion-matrices-basic-evaluation-metrics.en.txt
07_practical-guide-to-controlled-experiments-on-the-web-optional_2007GuideControlledExperiments.pdf
01_model-evaluation-selection.en.srt
09_module-3-quiz_exam.html
08_model-selection-optimizing-classifiers-for-different-evaluation-metrics.mp4
07_practical-guide-to-controlled-experiments-on-the-web-optional_instructions.html
01_model-evaluation-selection.en.txt
08_model-selection-optimizing-classifiers-for-different-evaluation-metrics.en.srt
02_confusion-matrices-basic-evaluation-metrics.en.srt
05_multi-class-evaluation.en.srt
03_classifier-decision-functions.en.srt
08_model-selection-optimizing-classifiers-for-different-evaluation-metrics.en.txt
05_multi-class-evaluation.en.txt
06_regression-evaluation.en.srt
04_precision-recall-and-roc-curves.en.srt
03_classifier-decision-functions.en.txt
06_regression-evaluation.en.txt
04_precision-recall-and-roc-curves.en.txt
02_confusion-matrices-basic-evaluation-metrics.mp4
05_multi-class-evaluation.mp4
06_regression-evaluation.mp4
03_classifier-decision-functions.mp4
04_precision-recall-and-roc-curves.mp4
02_assignment-3
01_assignment-3-submission_instructions.html
01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn
01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn
04_key-concepts-in-machine-learn
02_introduction.en.txt
03_help-us-learn-more-about-you_
06_notice-for-auditing-learners-
10_zachary-lipton-the-foundation
09_k-nearest-neighbors-classific
11_module-1-quiz_exam.html
02_introduction.en.srt
01_syllabus_instructions.html
07_an-example-machine-learning-p
08_examining-the-data.en.srt
08_examining-the-data.mp4
08_examining-the-data.en.txt
05_python-tools-for-machine-lear
02_introduction.mp4
02_assignment-1
01_assignment-1-submission_instructions.html
python-data-analysis
01_fundamentals-of-data-manipulation-with-python
02_fundamentals-of-data-manipulation
02_manipulating-text-with-regular-expression.en.srt
01_numerical-python-library-numpy.mp4
02_manipulating-text-with-regular-expression.mp4
01_numerical-python-library-numpy.en.srt
02_manipulating-text-with-regular-expression.en.txt
03_regular-expression-operations-documentation_instructions.html
01_numerical-python-library-numpy.en.txt
03_regular-expression-operations-documentation_re.html
01_introduction-to-the-course
04_notice-for-auditing-learners-assignment-submission_instructions.html
05_help-us-learn-more-about-you_instructions.html
07_week-1-textbook-reading-assignment-optional_instructions.html
08_50-years-of-data-science-david-donoho-optional_instructions.html
13_python-dates-and-times.en.srt
13_python-dates-and-times.en.txt
09_python-functions.en.srt
15_advanced-python-lambda-and-list-comprehensions.en.txt
08_50-years-of-data-science-david-donoho-optional_50YearsDataScience.pdf
09_python-functions.mp4
10_python-types-and-sequences.en.srt
06_the-coursera-jupyter-notebook-system.en.srt
03_syllabus_instructions.html
14_advanced-python-objects-map.en.srt
09_python-functions.en.txt
10_python-types-and-sequences.en.txt
02_introduction-to-the-course.en.srt
06_the-coursera-jupyter-notebook-system.en.txt
01_introduction-to-specialization.en.srt
14_advanced-python-objects-map.en.txt
12_python-demonstration-reading-and-writing-csv-files.en.srt
11_python-more-on-strings.en.srt
15_advanced-python-lambda-and-list-comprehensions.en.srt
02_introduction-to-the-course.en.txt
01_introduction-to-specialization.en.txt
12_python-demonstration-reading-and-writing-csv-files.en.txt
11_python-more-on-strings.en.txt
10_python-types-and-sequences.mp4
02_introduction-to-the-course.mp4
14_advanced-python-objects-map.mp4
06_the-coursera-jupyter-notebook-system.mp4
01_introduction-to-specialization.mp4
11_python-more-on-strings.mp4
15_advanced-python-lambda-and-list-comprehensions.mp4
13_python-dates-and-times.mp4
12_python-demonstration-reading-and-writing-csv-files.mp4
03_week-1-assignment
02_assignment-1_instructions.html
01_quiz-1_exam.html
05_Resources
01_jupyter-notebook-faq
01__resources.html
01__Course_1_-_Notebook_Resources_1.zip
05_acknowledgements
01__resources.html
02_code-sharing-policy
01__resources.html
04_additional-python-resources
01__resources.html
01__classes.html
03_grading-system-faq
01__resources.html
02_basic-data-processing-with-pandas
01_introduction-to-pandas-and-series-data
01_week-2-reading-assignments-optional_instructions.html
04_querying-a-series.en.srt
03_the-series-data-structure.en.srt
04_querying-a-series.en.txt
03_the-series-data-structure.en.txt
02_introduction-to-pandas.en.srt
02_introduction-to-pandas.en.txt
04_querying-a-series.mp4
03_the-series-data-structure.mp4
02_introduction-to-pandas.mp4
03_week-2-assignment
02_assignment-2_instructions.html
01_quiz-2_exam.html
02_dataframe
03_querying-a-dataframe.en.srt
01_dataframe-data-structure.en.srt
05_missing-values.en.srt
02_dataframe-indexing-and-loading.en.srt
04_indexing-dataframes.en.srt
06_example-manipulating-dataframe.en.srt
01_dataframe-data-structure.en.txt
05_missing-values.en.txt
03_querying-a-dataframe.en.txt
06_example-manipulating-dataframe.en.txt
02_dataframe-indexing-and-loading.en.txt
04_indexing-dataframes.en.txt
01_dataframe-data-structure.mp4
05_missing-values.mp4
03_querying-a-dataframe.mp4
06_example-manipulating-dataframe.mp4
02_dataframe-indexing-and-loading.mp4
04_indexing-dataframes.mp4
04_answering-questions-with-messy-data
01_beyond-data-manipulation
01_basic-statistical-testing.en.srt
03_science-isn-t-broken-p-hacking_instructions.html
04_goodharts-law-optional_instructions.html
05_the-5-graph-algorithms-that-you-should-know_instructions.html
01_basic-statistical-testing.en.txt
02_other-forms-of-structured-data.en.srt
02_other-forms-of-structured-data.en.txt
02_other-forms-of-structured-data.mp4
01_basic-statistical-testing.mp4
02_week-4-assignment
04_keep-learning-with-michigan-online_instructions.html
01_assignment-4_instructions.html
03_post-course-survey_instructions.html
02_final-quiz_exam.html
03_more-data-processing-with-pandas
01_more-data-processing-with-pandas
01_week-3-reading-assignments-optional_instructions.html
04_group-by.en.srt
03_pandas-idioms.en.srt
02_merging-dataframes.en.srt
05_scales.en.srt
07_date-time-functionality.en.srt
04_group-by.en.txt
06_pivot-table.en.srt
03_pandas-idioms.en.txt
02_merging-dataframes.en.txt
07_date-time-functionality.en.txt
05_scales.en.txt
06_pivot-table.en.txt
04_group-by.mp4
03_pandas-idioms.mp4
05_scales.mp4
02_merging-dataframes.mp4
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