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O`REILLY - Data Science Bookcamp, VIDEO EDITION

  • 收录时间:2022-03-31 10:42:50
  • 文件大小:6GB
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  • 最近下载:2022-03-31 10:42:50
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文件列表

  1. 113 - Chapter 21. Measuring feature importance with coefficients.mp4 93MB
  2. 71 - Chapter 15. Clustering texts by topic, Part 2.mp4 87MB
  3. 66 - Chapter 15. Vectorizing documents using scikit-learn.mp4 87MB
  4. 86 - Case study 5 - Predicting future friendships from social network data.mp4 80MB
  5. 23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4 80MB
  6. 14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4 76MB
  7. 98 - Chapter 19. Community detection using Markov clustering, Part 2.mp4 75MB
  8. 93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp4 75MB
  9. 87 - Chapter 18. An introduction to graph theory and network analysis.mp4 75MB
  10. 16 - Chapter 5. Variance as a measure of dispersion.mp4 74MB
  11. 70 - Chapter 15. Clustering texts by topic, Part 1.mp4 73MB
  12. 109 - Chapter 21. Training a linear classifier, Part 2.mp4 73MB
  13. 81 - Chapter 17. Filtering jobs by relevance.mp4 73MB
  14. 44 - Chapter 12. Visualizing and clustering the extracted location data.mp4 71MB
  15. 84 - Chapter 17. Exploring clusters at alternative values of K.mp4 69MB
  16. 42 - Chapter 11. Limitations of the GeoNamesCache library.mp4 69MB
  17. 36 - Chapter 10. Clustering based on non-Euclidean distance.mp4 69MB
  18. 64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp4 69MB
  19. 22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4 68MB
  20. 82 - Chapter 17. Clustering skills in relevant job postings.mp4 67MB
  21. 05 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4 66MB
  22. 114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4 65MB
  23. 58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp4 65MB
  24. 20 - Chapter 6. Computing the area beneath a normal curve.mp4 65MB
  25. 128 - Chapter 23. Interpreting the trained model.mp4 64MB
  26. 106 - Chapter 20. Limitations of the KNN algorithm.mp4 63MB
  27. 76 - Chapter 16. The structure of HTML documents.mp4 63MB
  28. 41 - Chapter 11. Location tracking using GeoNamesCache.mp4 62MB
  29. 126 - Chapter 23. Adding profile features to the model.mp4 62MB
  30. 55 - Chapter 14. Dimension reduction of matrix data.mp4 62MB
  31. 33 - Chapter 10. Clustering data into groups.mp4 61MB
  32. 34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4 61MB
  33. 03 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4 61MB
  34. 69 - Chapter 15. Computing similarities across large document datasets.mp4 60MB
  35. 97 - Chapter 19. Community detection using Markov clustering, Part 1.mp4 60MB
  36. 80 - Chapter 17. Exploring the HTML for skill descriptions.mp4 60MB
  37. 119 - Chapter 22. Studying cancerous cells using feature importance.mp4 59MB
  38. 72 - Chapter 15. Visualizing text clusters.mp4 59MB
  39. 74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp4 59MB
  40. 17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4 59MB
  41. 40 - Chapter 11. Visualizing maps.mp4 58MB
  42. 107 - Chapter 21. Training linear classifiers with logistic regression.mp4 58MB
  43. 99 - Chapter 19. Uncovering friend groups in social networks.mp4 58MB
  44. 117 - Chapter 22. Training if_else models with more than two features.mp4 58MB
  45. 08 - Chapter 3. Deriving probabilities from histograms.mp4 58MB
  46. 90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp4 57MB
  47. 120 - Chapter 22. Improving performance using random forest classification.mp4 57MB
  48. 116 - Chapter 22. Deciding which feature to split on.mp4 57MB
  49. 02 - Chapter 1. Computing probabilities using Python This section covers.mp4 57MB
  50. 67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp4 57MB
  51. 103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4 55MB
  52. 19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4 55MB
  53. 59 - Chapter 14. Clustering 4D data in two dimensions.mp4 54MB
  54. 125 - Chapter 23. Training a predictive model using network features, Part 2.mp4 54MB
  55. 04 - Chapter 2. Plotting probabilities using Matplotlib.mp4 54MB
  56. 24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4 53MB
  57. 115 - Chapter 22. Training a nested if_else model using two features.mp4 53MB
  58. 89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp4 53MB
  59. 09 - Chapter 3. Computing histograms in NumPy.mp4 53MB
  60. 121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4 53MB
  61. 25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4 53MB
  62. 124 - Chapter 23. Training a predictive model using network features, Part 1.mp4 53MB
  63. 35 - Chapter 10. Using density to discover clusters.mp4 52MB
  64. 118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4 52MB
  65. 73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp4 51MB
  66. 112 - Chapter 21. Training linear classifiers using scikit-learn.mp4 50MB
  67. 101 - Chapter 20. The basics of supervised machine learning.mp4 49MB
  68. 92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp4 49MB
  69. 100 - Chapter 20. Network-driven supervised machine learning.mp4 49MB
  70. 52 - Chapter 13. Basic matrix operations, Part 1.mp4 49MB
  71. 50 - Chapter 13. Using normalization to improve TF vector similarity.mp4 49MB
  72. 95 - Chapter 19. Deriving PageRank centrality from probability theory.mp4 48MB
  73. 68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp4 48MB
  74. 54 - Chapter 13. Computational limits of matrix multiplication.mp4 48MB
  75. 61 - Chapter 14. Computing principal components without rotation.mp4 48MB
  76. 07 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp4 48MB
  77. 65 - Chapter 15. NLP analysis of large text datasets.mp4 47MB
  78. 12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4 47MB
  79. 78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp4 47MB
  80. 38 - Chapter 11. Geographic location visualization and analysis.mp4 47MB
  81. 62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp4 45MB
  82. 96 - Chapter 19. Computing PageRank centrality using NetworkX.mp4 45MB
  83. 49 - Chapter 13. Vectorizing texts using word counts.mp4 44MB
  84. 47 - Chapter 13. Simple text comparison.mp4 44MB
  85. 26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp4 44MB
  86. 31 - Chapter 9. Determining statistical significance.mp4 44MB
  87. 108 - Chapter 21. Training a linear classifier, Part 1.mp4 44MB
  88. 110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4 43MB
  89. 111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4 43MB
  90. 127 - Chapter 23. Optimizing performance across a steady set of features.mp4 43MB
  91. 48 - Chapter 13. Replacing words with numeric values.mp4 42MB
  92. 51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp4 42MB
  93. 83 - Chapter 17. Investigating the technical skill clusters.mp4 41MB
  94. 85 - Chapter 17. Analyzing the 700 most relevant postings.mp4 41MB
  95. 27 - Chapter 8. Analyzing tables using Pandas.mp4 41MB
  96. 37 - Chapter 10. Analyzing clusters using Pandas.mp4 40MB
  97. 77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp4 40MB
  98. 29 - Chapter 8. Saving and loading table data.mp4 40MB
  99. 94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp4 40MB
  100. 75 - Chapter 16. Extracting text from web pages.mp4 40MB
  101. 105 - Chapter 20. Running a grid search using scikit-learn.mp4 39MB
  102. 21 - Chapter 7. Statistical hypothesis testing.mp4 39MB
  103. 123 - Chapter 23. Exploring the experimental observations.mp4 39MB
  104. 56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp4 39MB
  105. 28 - Chapter 8. Retrieving table rows.mp4 38MB
  106. 57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp4 38MB
  107. 79 - Chapter 17. Case study 4 solution.mp4 37MB
  108. 102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4 37MB
  109. 15 - Chapter 5. Mean as a measure of centrality.mp4 37MB
  110. 06 - Chapter 3. Running random simulations in NumPy.mp4 36MB
  111. 46 - Chapter 13. Measuring text similarities.mp4 36MB
  112. 104 - Chapter 20. Optimizing KNN performance.mp4 36MB
  113. 10 - Chapter 3. Using permutations to shuffle cards.mp4 35MB
  114. 43 - Chapter 12. Case study 3 solution.mp4 35MB
  115. 63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp4 34MB
  116. 11 - Chapter 4. Case study 1 solution.mp4 34MB
  117. 30 - Chapter 9. Case study 2 solution.mp4 34MB
  118. 39 - Chapter 11. Plotting maps using Cartopy.mp4 33MB
  119. 122 - Chapter 23. Case study 5 solution.mp4 33MB
  120. 91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp4 32MB
  121. 18 - Chapter 6. Comparing two sampled normal curves.mp4 31MB
  122. 13 - Case study 2 - Assessing online ad clicks for significance.mp4 31MB
  123. 88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp4 31MB
  124. 60 - Chapter 14. Limitations of PCA.mp4 31MB
  125. 53 - Chapter 13. Basic matrix operations, Part 2.mp4 27MB
  126. 45 - Case study 4 - Using online job postings to improve your data science resume.mp4 24MB
  127. 01 - Case study 1 - Finding the winning strategy in a card game.mp4 7MB
  128. 32 - Case study 3 - Tracking disease outbreaks using news headlines.mp4 7MB