Manning Data science bookcamp hevc 2021 EN

1. 001 Case study 1 - Finding the winning strategy in a card game.m4v785.75KB
2. 002 Ch1. Computing probabilities using Python This section covers.m4v5.62MB
3. 003 Ch1. Problem 2 - Analyzing multiple die rolls.m4v6.17MB
4. 004 Ch2. Plotting probabilities using Matplotlib.m4v5.76MB
5. 005 Ch2. Comparing multiple coin-flip probability distributions.m4v6.27MB
6. 006 Ch3. Running random simulations in NumPy.m4v3.71MB
7. 007 Ch3. Computing confidence intervals using histograms and NumPy arrays.m4v5.09MB
8. 008 Ch3. Deriving probabilities from histograms.m4v5.59MB
9. 009 Ch3. Computing histograms in NumPy.m4v5.19MB
10. 010 Ch3. Using permutations to shuffle cards.m4v3.59MB
11. 011 Ch4. Case study 1 solution.m4v3.68MB
12. 012 Ch4. Optimizing strategies using the sample space for a 10-card deck.m4v3.93MB
13. 013 Case study 2 - Assessing online ad clicks for significance.m4v2.92MB
14. 014 Ch5. Basic probability and statistical analysis using SciPy.m4v6.13MB
15. 015 Ch5. Mean as a measure of centrality.m4v4.7MB
16. 016 Ch5. Variance as a measure of dispersion.m4v6.72MB
17. 017 Ch6. Making predictions using the central limit theorem and SciPy.m4v5.06MB
18. 018 Ch6. Comparing two sampled normal curves.m4v3.57MB
19. 019 Ch6. Determining the mean and variance of a population through random sampling.m4v5.59MB
20. 020 Ch6. Computing the area beneath a normal curve.m4v5.64MB
21. 021 Ch7. Statistical hypothesis testing.m4v3.79MB
22. 022 Ch7. Assessing the divergence between sample mean and population mean.m4v4.83MB
23. 023 Ch7. Data dredging - Coming to false conclusions through oversampling.m4v5.85MB
24. 024 Ch7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.m4v4.65MB
25. 025 Ch7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.m4v4.71MB
26. 026 Ch7. Permutation testing - Comparing means of samples when the population parameters are unknown.m4v4.14MB
27. 027 Ch8. Analyzing tables using Pandas.m4v4.89MB
28. 028 Ch8. Retrieving table rows.m4v4.33MB
29. 029 Ch8. Saving and loading table data.m4v3.8MB
30. 030 Ch9. Case study 2 solution.m4v3.56MB
31. 031 Ch9. Determining statistical significance.m4v3.82MB
32. 032 Case study 3 - Tracking disease outbreaks using news headlines.m4v772.36KB
33. 033 Ch10. Clustering data into groups.m4v5.87MB
34. 034 Ch10. K-means - A clustering algorithm for grouping data into K central groups.m4v5.73MB
35. 035 Ch10. Using density to discover clusters.m4v4.96MB
36. 036 Ch10. Clustering based on non-Euclidean distance.m4v4.87MB
37. 037 Ch10. Analyzing clusters using Pandas.m4v3.06MB
38. 038 Ch11. Geographic location visualization and analysis.m4v4.49MB
39. 039 Ch11. Plotting maps using Cartopy.m4v3.3MB
40. 040 Ch11. Visualizing maps.m4v6.38MB
41. 041 Ch11. Location tracking using GeoNamesCache.m4v6.02MB
42. 042 Ch11. Limitations of the GeoNamesCache library.m4v6.63MB
43. 043 Ch12. Case study 3 solution.m4v3.68MB
44. 044 Ch12. Visualizing and clustering the extracted location data.m4v6.68MB
45. 045 Case study 4 - Using online job postings to improve your data science resume.m4v2.35MB
46. 046 Ch13. Measuring text similarities.m4v3.73MB
47. 047 Ch13. Simple text comparison.m4v4.82MB
48. 048 Ch13. Replacing words with numeric values.m4v4.44MB
49. 049 Ch13. Vectorizing texts using word counts.m4v4.67MB
50. 050 Ch13. Using normalization to improve TF vector similarity.m4v4.32MB
51. 051 Ch13. Using unit vector dot products to convert between relevance metrics.m4v3.99MB
52. 052 Ch13. Basic matrix operations, Part 1.m4v5.3MB
53. 053 Ch13. Basic matrix operations, Part 2.m4v3.4MB
54. 054 Ch13. Computational limits of matrix multiplication.m4v4.47MB
55. 055 Ch14. Dimension reduction of matrix data.m4v5.47MB
56. 056 Ch14. Reducing dimensions using rotation, Part 1.m4v4.04MB
57. 057 Ch14. Reducing dimensions using rotation, Part 2.m4v3.56MB
58. 058 Ch14. Dimension reduction using PCA and scikit-learn.m4v6.43MB
59. 059 Ch14. Clustering 4D data in two dimensions.m4v4.85MB
60. 060 Ch14. Limitations of PCA.m4v3.12MB
61. 061 Ch14. Computing principal components without rotation.m4v4.7MB
62. 062 Ch14. Extracting eigenvectors using power iteration, Part 1.m4v4.38MB
63. 063 Ch14. Extracting eigenvectors using power iteration, Part 2.m4v3.5MB
64. 064 Ch14. Efficient dimension reduction using SVD and scikit-learn.m4v5.18MB
65. 065 Ch15. NLP analysis of large text datasets.m4v4.49MB
66. 066 Ch15. Vectorizing documents using scikit-learn.m4v7.16MB
67. 067 Ch15. Ranking words by both post frequency and count, Part 1.m4v4.98MB
68. 068 Ch15. Ranking words by both post frequency and count, Part 2.m4v4.57MB
69. 069 Ch15. Computing similarities across large document datasets.m4v5.26MB
70. 070 Ch15. Clustering texts by topic, Part 1.m4v6.09MB
71. 071 Ch15. Clustering texts by topic, Part 2.m4v6.87MB
72. 072 Ch15. Visualizing text clusters.m4v5.66MB
73. 073 Ch15. Using subplots to display multiple word clouds, Part 1.m4v4.17MB
74. 074 Ch15. Using subplots to display multiple word clouds, Part 2.m4v4.37MB
75. 075 Ch16. Extracting text from web pages.m4v4.04MB
76. 076 Ch16. The structure of HTML documents.m4v5.34MB
77. 077 Ch16. Parsing HTML using Beautiful Soup, Part 1.m4v4.44MB
78. 078 Ch16. Parsing HTML using Beautiful Soup, Part 2.m4v3.78MB
79. 079 Ch17. Case study 4 solution.m4v3.56MB
80. 080 Ch17. Exploring the HTML for skill descriptions.m4v4.71MB
81. 081 Ch17. Filtering jobs by relevance.m4v7MB
82. 082 Ch17. Clustering skills in relevant job postings.m4v6.2MB
83. 083 Ch17. Investigating the technical skill clusters.m4v4.13MB
84. 084 Ch17. Exploring clusters at alternative values of K.m4v5.22MB
85. 085 Ch17. Analyzing the 700 most relevant postings.m4v3.73MB
86. 086 Case study 5 - Predicting future friendships from social network data.m4v6.84MB
87. 087 Ch18. An introduction to graph theory and network analysis.m4v6.05MB
88. 088 Ch18. Analyzing web networks using NetworkX, Part 1.m4v3.88MB
89. 089 Ch18. Analyzing web networks using NetworkX, Part 2.m4v4.64MB
90. 090 Ch18. Utilizing undirected graphs to optimize the travel time between towns.m4v5.65MB
91. 091 Ch18. Computing the fastest travel time between nodes, Part 1.m4v3.13MB
92. 092 Ch18. Computing the fastest travel time between nodes, Part 2.m4v4.11MB
93. 093 Ch19. Dynamic graph theory techniques for node ranking and social network analysis.m4v6.71MB
94. 094 Ch19. Computing travel probabilities using matrix multiplication.m4v3.58MB
95. 095 Ch19. Deriving PageRank centrality from probability theory.m4v4.29MB
96. 096 Ch19. Computing PageRank centrality using NetworkX.m4v3.85MB
97. 097 Ch19. Community detection using Markov clustering, Part 1.m4v5.93MB
98. 098 Ch19. Community detection using Markov clustering, Part 2.m4v6.74MB
99. 099 Ch19. Uncovering friend groups in social networks.m4v4.77MB
100. 100 Ch20. Network-driven supervised machine learning.m4v4.33MB
101. 101 Ch20. The basics of supervised machine learning.m4v4.29MB
102. 102 Ch20. Measuring predicted label accuracy, Part 1.m4v4.74MB
103. 103 Ch20. Measuring predicted label accuracy, Part 2.m4v5.44MB
104. 104 Ch20. Optimizing KNN performance.m4v3.89MB
105. 105 Ch20. Running a grid search using scikit-learn.m4v4.26MB
106. 106 Ch20. Limitations of the KNN algorithm.m4v4.88MB
107. 107 Ch21. Training linear classifiers with logistic regression.m4v5.63MB
108. 108 Ch21. Training a linear classifier, Part 1.m4v4.74MB
109. 109 Ch21. Training a linear classifier, Part 2.m4v6.3MB
110. 110 Ch21. Improving linear classification with logistic regression, Part 1.m4v4.26MB
111. 111 Ch21. Improving linear classification with logistic regression, Part 2.m4v3.88MB
112. 112 Ch21. Training linear classifiers using scikit-learn.m4v4.75MB
113. 113 Ch21. Measuring feature importance with coefficients.m4v7.38MB
114. 114 Ch22. Training nonlinear classifiers with decision tree techniques.m4v6.36MB
115. 115 Ch22. Training a nested if_else model using two features.m4v5.34MB
116. 116 Ch22. Deciding which feature to split on.m4v5.96MB
117. 117 Ch22. Training if_else models with more than two features.m4v5.38MB
118. 118 Ch22. Training decision tree classifiers using scikit-learn.m4v4.95MB
119. 119 Ch22. Studying cancerous cells using feature importance.m4v5.41MB
120. 120 Ch22. Improving performance using random forest classification.m4v5.12MB
121. 121 Ch22. Training random forest classifiers using scikit-learn.m4v4.31MB
122. 122 Ch23. Case study 5 solution.m4v3.61MB
123. 123 Ch23. Exploring the experimental observations.m4v4.09MB
124. 124 Ch23. Training a predictive model using network features, Part 1.m4v3.98MB
125. 125 Ch23. Training a predictive model using network features, Part 2.m4v4.13MB
126. 126 Ch23. Adding profile features to the model.m4v5.21MB
127. 127 Ch23. Optimizing performance across a steady set of features.m4v4.03MB
128. 128 Ch23. Interpreting the trained model.m4v4.55MB