Applied Data Science with Python Specialization

/.../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

/

TutsNode.net.txt

[TGx]Downloaded from torrentgalaxy.to .txt

/.../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_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_instructions.html

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

/.pad/

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

/.../01_module-4-supervised-machine-learning-part-2/

12_leakage-in-data-mining-formulation-detection-and-avoidance-optional_cs670_Tran_PreferredPaper_LeakingInDataMining.pdf

02_random-forests.en.txt

14_rules-of-machine-learning-best-practices-for-ml-engineering-optional_rules_of_ml.pdf

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-optional_assisting-pathologists-in-detecting.html

04_neural-networks.en.srt

06_play-with-neural-networks-tensorflow-playground-optional_instructions.html

08_deep-learning-in-a-nutshell-core-concepts-optional_instructions.html

09_assisting-pathologists-in-detecting-cancer-with-deep-learning-optional_instructions.html

11_the-treachery-of-leakage-optional_instructions.html

12_leakage-in-data-mining-formulation-detection-and-avoidance-optional_instructions.html

13_data-leakage-example-the-icml-2013-whale-challenge-optional_instructions.html

14_rules-of-machine-learning-best-practices-for-ml-engineering-optional_instructions.html

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

/.../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_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

/.../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

/.../02_jupyter-notebook-faq/

01__resources.html

/.../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

/.../01_jupyter-notebook-faq/

01__resources.html

/.../01_principles-of-information-visualization/

11_graphical-heuristics-lie-factor-and-spark-lines-edward-tufte.en.txt

12_the-truthful-art-alberto-cairo.mp4

02_syllabus_Cairo2015_Chapter_GraphicsLiesMisleadingVisuals.pdf

07_graphical-heuristics-data-ink-ratio-edward-tufte.en.txt

05_tools-for-thinking-about-design-alberto-cairo.mp4

03_help-us-learn-more-about-you_instructions.html

04_about-the-professor-christopher-brooks.en.srt

04_about-the-professor-christopher-brooks.en.txt

06_notice-for-coursera-learners-assignment-submission_instructions.html

08_dark-horse-analytics-optional_instructions.html

10_useful-junk-the-effects-of-visual-embellishment-on-comprehension-and_instructions.html

12_the-truthful-art-alberto-cairo.en.srt

05_tools-for-thinking-about-design-alberto-cairo.en.srt

02_syllabus_instructions.html

01_introduction.en.txt

12_the-truthful-art-alberto-cairo.en.txt

05_tools-for-thinking-about-design-alberto-cairo.en.txt

09_graphical-heuristics-chart-junk-edward-tufte.en.srt

07_graphical-heuristics-data-ink-ratio-edward-tufte.en.srt

01_introduction.en.srt

11_graphical-heuristics-lie-factor-and-spark-lines-edward-tufte.en.srt

09_graphical-heuristics-chart-junk-edward-tufte.en.txt

09_graphical-heuristics-chart-junk-edward-tufte.mp4

01_introduction.mp4

11_graphical-heuristics-lie-factor-and-spark-lines-edward-tufte.mp4

07_graphical-heuristics-data-ink-ratio-edward-tufte.mp4

04_about-the-professor-christopher-brooks.mp4

/.../01_jupyter-notebook-faq/

01__resources.html

/.../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

/.../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

/.../01_jupyter-notebook-faq/

01__resources.html

01__Course_1_-_Notebook_Resources_1.zip

/.../01_module-2-basic-charting/

03_matplotlib_matplotlib.html

09_dejunkifying-a-plot.en.txt

09_dejunkifying-a-plot.en.srt

06_scatterplots.en.txt

06_scatterplots.mp4

02_matplotlib-architecture.mp4

01_introduction.en.srt

01_introduction.en.txt

04_ten-simple-rules-for-better-figures_instructions.html

05_basic-plotting-with-matplotlib.en.srt

07_line-plots.en.srt

06_scatterplots.en.srt

02_matplotlib-architecture.en.srt

07_line-plots.en.txt

03_matplotlib_instructions.html

02_matplotlib-architecture.en.txt

05_basic-plotting-with-matplotlib.en.txt

08_bar-charts.en.srt

08_bar-charts.en.txt

07_line-plots.mp4

09_dejunkifying-a-plot.mp4

05_basic-plotting-with-matplotlib.mp4

08_bar-charts.mp4

01_introduction.mp4

/.../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

/.../01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/

04_key-concepts-in-machine-learning.mp4

02_introduction.en.txt

03_help-us-learn-more-about-you_instructions.html

06_notice-for-auditing-learners-assignment-submission_instructions.html

10_zachary-lipton-the-foundations-of-algorithmic-bias-optional_instructions.html

09_k-nearest-neighbors-classification.mp4

11_module-1-quiz_exam.html

09_k-nearest-neighbors-classification.en.srt

04_key-concepts-in-machine-learning.en.srt

09_k-nearest-neighbors-classification.en.txt

02_introduction.en.srt

01_syllabus_instructions.html

07_an-example-machine-learning-problem.en.srt

08_examining-the-data.en.srt

04_key-concepts-in-machine-learning.en.txt

08_examining-the-data.mp4

07_an-example-machine-learning-problem.en.txt

08_examining-the-data.en.txt

05_python-tools-for-machine-learning.en.srt

07_an-example-machine-learning-problem.mp4

05_python-tools-for-machine-learning.en.txt

02_introduction.mp4

05_python-tools-for-machine-learning.mp4

/.../03_week-1-assignment/

02_assignment-1_instructions.html

01_quiz-1_exam.html

/.../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

/.../01_additional-resources/

01__documentation.html

01__Scikit_Learn_Cheat_Sheet_Python.pdf

01__classes.html

01__resources.html

/.../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

/.../03_course-slides/

01__Week2_Basic_Charting.pptx

01__Week3_Slides_Final.pdf

01__Week2_Slides_Final.pdf

01__Week3Slides.pptx

01__resources.html

01__Week1Slides.pptx

01__Week1_Slides_Final.pdf

/.../python-data-analysis/05_Resources/05_acknowledgements/

01__resources.html

/.../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

/.../03_week-2-assignment/

02_assignment-2_instructions.html

01_quiz-2_exam.html

/.../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

07_date-time-functionality.mp4

06_pivot-table.mp4

/.../01_module-3-charting-fundamentals/

07_interactivity.en.txt

03_selecting-the-number-of-bins-in-a-histogram-a-decision-theoretic-approach_hist.pdf

02_histograms.en.srt

01_subplots.en.srt

04_box-plots.en.srt

02_histograms.en.txt

07_interactivity.en.srt

06_animation.en.srt

01_subplots.en.txt

04_box-plots.en.txt

05_heatmaps.en.srt

06_animation.en.txt

05_heatmaps.en.txt

03_selecting-the-number-of-bins-in-a-histogram-a-decision-theoretic-approach_instructions.html

02_histograms.mp4

01_subplots.mp4

04_box-plots.mp4

07_interactivity.mp4

06_animation.mp4

05_heatmaps.mp4

/.../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

/.../04_acknowledgements-credits/

01__matplotlib.html

01__Cairo2015_Chapter_GraphicsLiesMisleadingVisuals.pdf

01__hist.pdf

01__resources.html

/.../02_week-3-assignment/

02_assignment-3_instructions.html

01_quiz-3_exam.html

/.../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

/.../02_basic-data-processing-with-pandas/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

/.../01_module-1-principles-of-information-visualization/02_assignment-1/

01_graphics-lies-misleading-visuals_BookChapterLIES.pdf

02_graphics-lies-misleading-visuals_peer_assignment_instructions.html

01_graphics-lies-misleading-visuals_Cairo2015_Chapter_GraphicsLiesMisleadingVisuals.pdf

02_graphics-lies-misleading-visuals_Cairo2015_Chapter_GraphicsLiesMisleadingVisuals.pdf

02_graphics-lies-misleading-visuals_assignment1_rubric.pdf

01_graphics-lies-misleading-visuals_instructions.html

/.../02_code-sharing-policy/

01__resources.html

/.../02_module-2-basic-charting/02_assignment-2/

01_plotting-weather-patterns_assignment2_rubric.pdf

01_plotting-weather-patterns_peer_assignment_instructions.html

/.../04_additional-python-resources/

01__resources.html

01__classes.html

/.../01_jupyter-notebook-faq/

01__resources.html

/.../02_additional-python-resources/

01__classes.html

01__resources.html

/.../04_module-4-supervised-machine-learning-part-2/04_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

/.../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_additional-resources/

01__documentation.html

01__Scikit_Learn_Cheat_Sheet_Python.pdf

01__classes.html

01__resources.html

/.../01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/02_assignment-1/

01_assignment-1-submission_instructions.html

/.../03_post-course-survey/

02_keep-learning-with-michigan-online_instructions.html

01_post-course-survey_instructions.html

/.../03_post-course-survey/

02_keep-learning-with-michigan-online_instructions.html

01_post-course-survey_instructions.html

/.../02_additional-resources/

01__Scikit_Learn_Cheat_Sheet_Python.pdf

01__intro.html

01__documentation.html

01__classes.html

01__resources.html

/.../04_module-4-applied-visualizations/02_project/

01_becoming-an-independent-data-scientist.en.srt

02_becoming-an-independent-data-scientist_assignment4_rubric.pdf

01_becoming-an-independent-data-scientist.en.txt

02_becoming-an-independent-data-scientist_peer_assignment_instructions.html

03_post-course-survey_instructions.html

01_becoming-an-independent-data-scientist.mp4

/.../02_module-2-supervised-machine-learning-part-1/02_assignment-2/

01_assignment-2-submission_instructions.html

/.../06_accessible-html-slides/

01__Week_1_Principles_of_Information_Visualization.html

01__Week_2_Basic_Charting.html

01__Week_3_Charting_Fundamentals.html

01__resources.html

/.../03_module-3-charting-fundamentals/02_assignment-3/

02_building-a-custom-visualization_assignment3_rubric.pdf

01_assignment-reading_instructions.html

02_building-a-custom-visualization_peer_assignment_instructions.html

03_understanding-error-bars_instructions.html

01_assignment-reading_p571-ferreira.pdf