O`REILLY - Data Science Bookcamp VIDEO EDITION

1. 01 - Case study 1 - Finding the winning strategy in a card game.mp46.89MB
2. 02 - Chapter 1. Computing probabilities using Python This section covers.mp456.75MB
3. 03 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp460.89MB
4. 04 - Chapter 2. Plotting probabilities using Matplotlib.mp453.74MB
5. 05 - Chapter 2. Comparing multiple coin-flip probability distributions.mp465.57MB
6. 06 - Chapter 3. Running random simulations in NumPy.mp436.35MB
7. 07 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp447.59MB
8. 08 - Chapter 3. Deriving probabilities from histograms.mp457.63MB
9. 09 - Chapter 3. Computing histograms in NumPy.mp452.99MB
10. 10 - Chapter 3. Using permutations to shuffle cards.mp435.4MB
11. 11 - Chapter 4. Case study 1 solution.mp434.27MB
12. 12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp447.1MB
13. 13 - Case study 2 - Assessing online ad clicks for significance.mp431.4MB
14. 14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp476.23MB
15. 15 - Chapter 5. Mean as a measure of centrality.mp436.58MB
16. 16 - Chapter 5. Variance as a measure of dispersion.mp473.89MB
17. 17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp458.61MB
18. 18 - Chapter 6. Comparing two sampled normal curves.mp431.46MB
19. 19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp455.19MB
20. 20 - Chapter 6. Computing the area beneath a normal curve.mp464.57MB
21. 21 - Chapter 7. Statistical hypothesis testing.mp439.19MB
22. 22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp468.3MB
23. 23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp479.88MB
24. 24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp453.28MB
25. 25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp452.78MB
26. 26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp443.69MB
27. 27 - Chapter 8. Analyzing tables using Pandas.mp440.87MB
28. 28 - Chapter 8. Retrieving table rows.mp438.24MB
29. 29 - Chapter 8. Saving and loading table data.mp440.28MB
30. 30 - Chapter 9. Case study 2 solution.mp433.6MB
31. 31 - Chapter 9. Determining statistical significance.mp443.58MB
32. 32 - Case study 3 - Tracking disease outbreaks using news headlines.mp46.6MB
33. 33 - Chapter 10. Clustering data into groups.mp461.4MB
34. 34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp461.2MB
35. 35 - Chapter 10. Using density to discover clusters.mp452.23MB
36. 36 - Chapter 10. Clustering based on non-Euclidean distance.mp468.79MB
37. 37 - Chapter 10. Analyzing clusters using Pandas.mp440.48MB
38. 38 - Chapter 11. Geographic location visualization and analysis.mp446.58MB
39. 39 - Chapter 11. Plotting maps using Cartopy.mp433.23MB
40. 40 - Chapter 11. Visualizing maps.mp458.27MB
41. 41 - Chapter 11. Location tracking using GeoNamesCache.mp462.35MB
42. 42 - Chapter 11. Limitations of the GeoNamesCache library.mp469.19MB
43. 43 - Chapter 12. Case study 3 solution.mp434.63MB
44. 44 - Chapter 12. Visualizing and clustering the extracted location data.mp470.72MB
45. 45 - Case study 4 - Using online job postings to improve your data science resume.mp423.95MB
46. 46 - Chapter 13. Measuring text similarities.mp436.28MB
47. 47 - Chapter 13. Simple text comparison.mp444MB
48. 48 - Chapter 13. Replacing words with numeric values.mp442.07MB
49. 49 - Chapter 13. Vectorizing texts using word counts.mp444.5MB
50. 50 - Chapter 13. Using normalization to improve TF vector similarity.mp448.56MB
51. 51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp441.64MB
52. 52 - Chapter 13. Basic matrix operations, Part 1.mp448.78MB
53. 53 - Chapter 13. Basic matrix operations, Part 2.mp427.15MB
54. 54 - Chapter 13. Computational limits of matrix multiplication.mp447.81MB
55. 55 - Chapter 14. Dimension reduction of matrix data.mp461.74MB
56. 56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp438.99MB
57. 57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp437.56MB
58. 58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp464.72MB
59. 59 - Chapter 14. Clustering 4D data in two dimensions.mp454.44MB
60. 60 - Chapter 14. Limitations of PCA.mp430.77MB
61. 61 - Chapter 14. Computing principal components without rotation.mp447.8MB
62. 62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp444.67MB
63. 63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp434.38MB
64. 64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp468.6MB
65. 65 - Chapter 15. NLP analysis of large text datasets.mp447.16MB
66. 66 - Chapter 15. Vectorizing documents using scikit-learn.mp487.06MB
67. 67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp456.59MB
68. 68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp448.13MB
69. 69 - Chapter 15. Computing similarities across large document datasets.mp460.24MB
70. 70 - Chapter 15. Clustering texts by topic, Part 1.mp473.3MB
71. 71 - Chapter 15. Clustering texts by topic, Part 2.mp487.08MB
72. 72 - Chapter 15. Visualizing text clusters.mp458.9MB
73. 73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp450.57MB
74. 74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp458.83MB
75. 75 - Chapter 16. Extracting text from web pages.mp439.55MB
76. 76 - Chapter 16. The structure of HTML documents.mp462.95MB
77. 77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp440.42MB
78. 78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp446.78MB
79. 79 - Chapter 17. Case study 4 solution.mp437.42MB
80. 80 - Chapter 17. Exploring the HTML for skill descriptions.mp459.65MB
81. 81 - Chapter 17. Filtering jobs by relevance.mp473.18MB
82. 82 - Chapter 17. Clustering skills in relevant job postings.mp466.54MB
83. 83 - Chapter 17. Investigating the technical skill clusters.mp441.46MB
84. 84 - Chapter 17. Exploring clusters at alternative values of K.mp469.37MB
85. 85 - Chapter 17. Analyzing the 700 most relevant postings.mp440.95MB
86. 86 - Case study 5 - Predicting future friendships from social network data.mp480.4MB
87. 87 - Chapter 18. An introduction to graph theory and network analysis.mp474.88MB
88. 88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp430.92MB
89. 89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp453.06MB
90. 90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp457.39MB
91. 91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp432.12MB
92. 92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp449.04MB
93. 93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp475.08MB
94. 94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp440.21MB
95. 95 - Chapter 19. Deriving PageRank centrality from probability theory.mp448.36MB
96. 96 - Chapter 19. Computing PageRank centrality using NetworkX.mp444.66MB
97. 97 - Chapter 19. Community detection using Markov clustering, Part 1.mp460.05MB
98. 98 - Chapter 19. Community detection using Markov clustering, Part 2.mp475.21MB
99. 99 - Chapter 19. Uncovering friend groups in social networks.mp457.99MB
100. 100 - Chapter 20. Network-driven supervised machine learning.mp448.95MB
101. 101 - Chapter 20. The basics of supervised machine learning.mp449.2MB
102. 102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp437.28MB
103. 103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp455.24MB
104. 104 - Chapter 20. Optimizing KNN performance.mp435.68MB
105. 105 - Chapter 20. Running a grid search using scikit-learn.mp439.33MB
106. 106 - Chapter 20. Limitations of the KNN algorithm.mp463.16MB
107. 107 - Chapter 21. Training linear classifiers with logistic regression.mp458.26MB
108. 108 - Chapter 21. Training a linear classifier, Part 1.mp443.52MB
109. 109 - Chapter 21. Training a linear classifier, Part 2.mp473.26MB
110. 110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp443.42MB
111. 111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp443.12MB
112. 112 - Chapter 21. Training linear classifiers using scikit-learn.mp449.64MB
113. 113 - Chapter 21. Measuring feature importance with coefficients.mp493.13MB
114. 114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp465.2MB
115. 115 - Chapter 22. Training a nested if_else model using two features.mp453.25MB
116. 116 - Chapter 22. Deciding which feature to split on.mp457.23MB
117. 117 - Chapter 22. Training if_else models with more than two features.mp457.79MB
118. 118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp451.86MB
119. 119 - Chapter 22. Studying cancerous cells using feature importance.mp459.29MB
120. 120 - Chapter 22. Improving performance using random forest classification.mp457.38MB
121. 121 - Chapter 22. Training random forest classifiers using scikit-learn.mp452.96MB
122. 122 - Chapter 23. Case study 5 solution.mp432.94MB
123. 123 - Chapter 23. Exploring the experimental observations.mp438.99MB
124. 124 - Chapter 23. Training a predictive model using network features, Part 1.mp452.59MB
125. 125 - Chapter 23. Training a predictive model using network features, Part 2.mp453.87MB
126. 126 - Chapter 23. Adding profile features to the model.mp462.03MB
127. 127 - Chapter 23. Optimizing performance across a steady set of features.mp442.55MB
128. 128 - Chapter 23. Interpreting the trained model.mp464.17MB