[FreeCoursesOnline.Me] O`REILLY - Data Science Bookcamp, video edition torrent, magnet, bt

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[FreeCoursesOnline.Me] O`REILLY - Data Science Bookcamp, video edition

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1 - Case study 1 - Finding the winning strategy in a card game.mp4

6.89MB

10 - Chapter 3. Using permutations to shuffle cards.mp4

35.4MB

100 - Chapter 20. Network-driven supervised machine learning.mp4

48.95MB

101 - Chapter 20. The basics of supervised machine learning.mp4

49.2MB

102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4

37.28MB

103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4

55.24MB

104 - Chapter 20. Optimizing KNN performance.mp4

35.68MB

105 - Chapter 20. Running a grid search using scikit-learn.mp4

39.33MB

106 - Chapter 20. Limitations of the KNN algorithm.mp4

63.16MB

107 - Chapter 21. Training linear classifiers with logistic regression.mp4

58.26MB

108 - Chapter 21. Training a linear classifier, Part 1.mp4

43.52MB

109 - Chapter 21. Training a linear classifier, Part 2.mp4

73.26MB

11 - Chapter 4. Case study 1 solution.mp4

34.27MB

110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4

43.42MB

111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4

43.12MB

112 - Chapter 21. Training linear classifiers using scikit-learn.mp4

49.64MB

113 - Chapter 21. Measuring feature importance with coefficients.mp4

93.13MB

114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4

65.2MB

115 - Chapter 22. Training a nested if_else model using two features.mp4

53.25MB

116 - Chapter 22. Deciding which feature to split on.mp4

57.23MB

117 - Chapter 22. Training if_else models with more than two features.mp4

57.79MB

118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4

51.86MB

119 - Chapter 22. Studying cancerous cells using feature importance.mp4

59.29MB

12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4

47.1MB

120 - Chapter 22. Improving performance using random forest classification.mp4

57.38MB

121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4

52.96MB

122 - Chapter 23. Case study 5 solution.mp4

32.94MB

123 - Chapter 23. Exploring the experimental observations.mp4

38.99MB

124 - Chapter 23. Training a predictive model using network features, Part 1.mp4

52.59MB

125 - Chapter 23. Training a predictive model using network features, Part 2.mp4

53.87MB

126 - Chapter 23. Adding profile features to the model.mp4

62.03MB

127 - Chapter 23. Optimizing performance across a steady set of features.mp4

42.55MB

128 - Chapter 23. Interpreting the trained model.mp4

64.17MB

13 - Case study 2 - Assessing online ad clicks for significance.mp4

31.4MB

14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4

76.23MB

15 - Chapter 5. Mean as a measure of centrality.mp4

36.58MB

16 - Chapter 5. Variance as a measure of dispersion.mp4

73.89MB

17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4

58.61MB

18 - Chapter 6. Comparing two sampled normal curves.mp4

31.46MB

19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4

55.19MB

2 - Chapter 1. Computing probabilities using Python This section covers.mp4

56.75MB

20 - Chapter 6. Computing the area beneath a normal curve.mp4

64.57MB

21 - Chapter 7. Statistical hypothesis testing.mp4

39.19MB

22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4

68.3MB

23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4

79.88MB

24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4

53.28MB

25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4

52.78MB

26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp4

43.69MB

27 - Chapter 8. Analyzing tables using Pandas.mp4

40.87MB

28 - Chapter 8. Retrieving table rows.mp4

38.24MB

29 - Chapter 8. Saving and loading table data.mp4

40.28MB

3 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4

60.89MB

30 - Chapter 9. Case study 2 solution.mp4

33.6MB

31 - Chapter 9. Determining statistical significance.mp4

43.58MB

32 - Case study 3 - Tracking disease outbreaks using news headlines.mp4

6.6MB

33 - Chapter 10. Clustering data into groups.mp4

61.4MB

34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4

61.2MB

35 - Chapter 10. Using density to discover clusters.mp4

52.23MB

36 - Chapter 10. Clustering based on non-Euclidean distance.mp4

68.79MB

37 - Chapter 10. Analyzing clusters using Pandas.mp4

40.48MB

38 - Chapter 11. Geographic location visualization and analysis.mp4

46.58MB

39 - Chapter 11. Plotting maps using Cartopy.mp4

33.23MB

4 - Chapter 2. Plotting probabilities using Matplotlib.mp4

53.74MB

40 - Chapter 11. Visualizing maps.mp4

58.27MB

41 - Chapter 11. Location tracking using GeoNamesCache.mp4

62.35MB

42 - Chapter 11. Limitations of the GeoNamesCache library.mp4

69.19MB

43 - Chapter 12. Case study 3 solution.mp4

34.63MB

44 - Chapter 12. Visualizing and clustering the extracted location data.mp4

70.72MB

45 - Case study 4 - Using online job postings to improve your data science resume.mp4

23.95MB

46 - Chapter 13. Measuring text similarities.mp4

36.28MB

47 - Chapter 13. Simple text comparison.mp4

44MB

48 - Chapter 13. Replacing words with numeric values.mp4

42.07MB

49 - Chapter 13. Vectorizing texts using word counts.mp4

44.5MB

5 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4

65.57MB

50 - Chapter 13. Using normalization to improve TF vector similarity.mp4

48.56MB

51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp4

41.64MB

52 - Chapter 13. Basic matrix operations, Part 1.mp4

48.78MB

53 - Chapter 13. Basic matrix operations, Part 2.mp4

27.15MB

54 - Chapter 13. Computational limits of matrix multiplication.mp4

47.81MB

55 - Chapter 14. Dimension reduction of matrix data.mp4

61.74MB

56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp4

38.99MB

57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp4

37.56MB

58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp4

64.72MB

59 - Chapter 14. Clustering 4D data in two dimensions.mp4

54.44MB

6 - Chapter 3. Running random simulations in NumPy.mp4

36.35MB

60 - Chapter 14. Limitations of PCA.mp4

30.77MB

61 - Chapter 14. Computing principal components without rotation.mp4

47.8MB

62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp4

44.67MB

63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp4

34.38MB

64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp4

68.6MB

65 - Chapter 15. NLP analysis of large text datasets.mp4

47.16MB

66 - Chapter 15. Vectorizing documents using scikit-learn.mp4

87.06MB

67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp4

56.59MB

68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp4

48.13MB

69 - Chapter 15. Computing similarities across large document datasets.mp4

60.24MB

7 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp4

47.59MB

70 - Chapter 15. Clustering texts by topic, Part 1.mp4

73.3MB

71 - Chapter 15. Clustering texts by topic, Part 2.mp4

87.08MB

72 - Chapter 15. Visualizing text clusters.mp4

58.9MB

73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp4

50.57MB

74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp4

58.83MB

75 - Chapter 16. Extracting text from web pages.mp4

39.55MB

76 - Chapter 16. The structure of HTML documents.mp4

62.95MB

77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp4

40.42MB

78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp4

46.78MB

79 - Chapter 17. Case study 4 solution.mp4

37.42MB

8 - Chapter 3. Deriving probabilities from histograms.mp4

57.63MB

80 - Chapter 17. Exploring the HTML for skill descriptions.mp4

59.65MB

81 - Chapter 17. Filtering jobs by relevance.mp4

73.18MB

82 - Chapter 17. Clustering skills in relevant job postings.mp4

66.54MB

83 - Chapter 17. Investigating the technical skill clusters.mp4

41.46MB

84 - Chapter 17. Exploring clusters at alternative values of K.mp4

69.37MB

85 - Chapter 17. Analyzing the 700 most relevant postings.mp4

40.95MB

86 - Case study 5 - Predicting future friendships from social network data.mp4

80.4MB

87 - Chapter 18. An introduction to graph theory and network analysis.mp4

74.88MB

88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp4

30.92MB

89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp4

53.06MB

9 - Chapter 3. Computing histograms in NumPy.mp4

52.99MB

90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp4

57.39MB

91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp4

32.12MB

92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp4

49.04MB

93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp4

75.08MB

94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp4

40.21MB

95 - Chapter 19. Deriving PageRank centrality from probability theory.mp4

48.36MB

96 - Chapter 19. Computing PageRank centrality using NetworkX.mp4

44.66MB

97 - Chapter 19. Community detection using Markov clustering, Part 1.mp4

60.05MB

98 - Chapter 19. Community detection using Markov clustering, Part 2.mp4

75.21MB

99 - Chapter 19. Uncovering friend groups in social networks.mp4

57.99MB

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