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0. OneHack.us Premium Cracked Accounts-Tutorials-Guides-Articles Community Based Forum.url
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URL
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409.6 B
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1 - Case study 1 - Finding the winning strategy in a card game.mp4
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MP4
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6.9 MB
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1. FreeCoursesOnline.Me Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url
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URL
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307.2 B
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10 - Chapter 3. Using permutations to shuffle cards.mp4
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MP4
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35.4 MB
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100 - Chapter 20. Network-driven supervised machine learning.mp4
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MP4
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49 MB
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101 - Chapter 20. The basics of supervised machine learning.mp4
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49.2 MB
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102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4
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MP4
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37.3 MB
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103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4
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MP4
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55.2 MB
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104 - Chapter 20. Optimizing KNN performance.mp4
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MP4
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35.7 MB
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105 - Chapter 20. Running a grid search using scikit-learn.mp4
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MP4
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39.3 MB
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106 - Chapter 20. Limitations of the KNN algorithm.mp4
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63.2 MB
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107 - Chapter 21. Training linear classifiers with logistic regression.mp4
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58.3 MB
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108 - Chapter 21. Training a linear classifier, Part 1.mp4
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43.5 MB
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109 - Chapter 21. Training a linear classifier, Part 2.mp4
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MP4
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73.3 MB
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11 - Chapter 4. Case study 1 solution.mp4
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MP4
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34.3 MB
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110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4
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43.4 MB
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111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4
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MP4
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43.1 MB
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112 - Chapter 21. Training linear classifiers using scikit-learn.mp4
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MP4
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49.6 MB
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113 - Chapter 21. Measuring feature importance with coefficients.mp4
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MP4
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93.1 MB
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114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4
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MP4
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65.2 MB
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115 - Chapter 22. Training a nested if_else model using two features.mp4
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MP4
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53.3 MB
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116 - Chapter 22. Deciding which feature to split on.mp4
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MP4
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57.2 MB
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117 - Chapter 22. Training if_else models with more than two features.mp4
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MP4
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57.8 MB
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118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4
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MP4
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51.9 MB
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119 - Chapter 22. Studying cancerous cells using feature importance.mp4
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MP4
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59.3 MB
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12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4
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MP4
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47.1 MB
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120 - Chapter 22. Improving performance using random forest classification.mp4
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MP4
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57.4 MB
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121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4
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MP4
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53 MB
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122 - Chapter 23. Case study 5 solution.mp4
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MP4
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32.9 MB
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123 - Chapter 23. Exploring the experimental observations.mp4
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MP4
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39 MB
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124 - Chapter 23. Training a predictive model using network features, Part 1.mp4
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MP4
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52.6 MB
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125 - Chapter 23. Training a predictive model using network features, Part 2.mp4
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MP4
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53.9 MB
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126 - Chapter 23. Adding profile features to the model.mp4
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MP4
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62 MB
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127 - Chapter 23. Optimizing performance across a steady set of features.mp4
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42.6 MB
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128 - Chapter 23. Interpreting the trained model.mp4
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MP4
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64.2 MB
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13 - Case study 2 - Assessing online ad clicks for significance.mp4
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MP4
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31.4 MB
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14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4
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MP4
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76.2 MB
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15 - Chapter 5. Mean as a measure of centrality.mp4
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36.6 MB
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16 - Chapter 5. Variance as a measure of dispersion.mp4
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MP4
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73.9 MB
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17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4
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58.6 MB
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18 - Chapter 6. Comparing two sampled normal curves.mp4
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31.5 MB
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19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4
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MP4
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55.2 MB
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2 - Chapter 1. Computing probabilities using Python This section covers.mp4
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MP4
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56.8 MB
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20 - Chapter 6. Computing the area beneath a normal curve.mp4
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64.6 MB
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21 - Chapter 7. Statistical hypothesis testing.mp4
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MP4
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39.2 MB
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22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4
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MP4
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68.3 MB
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23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4
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MP4
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79.9 MB
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24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4
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MP4
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53.3 MB
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25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4
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MP4
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52.8 MB
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26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp4
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MP4
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43.7 MB
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27 - Chapter 8. Analyzing tables using Pandas.mp4
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40.9 MB
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28 - Chapter 8. Retrieving table rows.mp4
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38.2 MB
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29 - Chapter 8. Saving and loading table data.mp4
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MP4
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40.3 MB
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3 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4
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MP4
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60.9 MB
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3. FTUApps.com Download Cracked Developers Applications For Free.url
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URL
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204.8 B
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30 - Chapter 9. Case study 2 solution.mp4
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MP4
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33.6 MB
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31 - Chapter 9. Determining statistical significance.mp4
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MP4
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43.6 MB
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32 - Case study 3 - Tracking disease outbreaks using news headlines.mp4
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MP4
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6.6 MB
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33 - Chapter 10. Clustering data into groups.mp4
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MP4
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61.4 MB
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34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4
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61.2 MB
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35 - Chapter 10. Using density to discover clusters.mp4
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52.2 MB
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36 - Chapter 10. Clustering based on non-Euclidean distance.mp4
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MP4
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68.8 MB
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37 - Chapter 10. Analyzing clusters using Pandas.mp4
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MP4
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40.5 MB
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38 - Chapter 11. Geographic location visualization and analysis.mp4
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46.6 MB
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39 - Chapter 11. Plotting maps using Cartopy.mp4
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33.2 MB
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4 - Chapter 2. Plotting probabilities using Matplotlib.mp4
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53.7 MB
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40 - Chapter 11. Visualizing maps.mp4
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MP4
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58.3 MB
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41 - Chapter 11. Location tracking using GeoNamesCache.mp4
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MP4
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62.3 MB
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42 - Chapter 11. Limitations of the GeoNamesCache library.mp4
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MP4
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69.2 MB
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43 - Chapter 12. Case study 3 solution.mp4
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MP4
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34.6 MB
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44 - Chapter 12. Visualizing and clustering the extracted location data.mp4
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MP4
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70.7 MB
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45 - Case study 4 - Using online job postings to improve your data science resume.mp4
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MP4
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23.9 MB
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46 - Chapter 13. Measuring text similarities.mp4
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MP4
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36.3 MB
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47 - Chapter 13. Simple text comparison.mp4
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MP4
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44 MB
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48 - Chapter 13. Replacing words with numeric values.mp4
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MP4
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42.1 MB
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49 - Chapter 13. Vectorizing texts using word counts.mp4
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MP4
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44.5 MB
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5 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4
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MP4
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65.6 MB
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50 - Chapter 13. Using normalization to improve TF vector similarity.mp4
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MP4
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48.6 MB
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51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp4
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MP4
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41.6 MB
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52 - Chapter 13. Basic matrix operations, Part 1.mp4
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MP4
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48.8 MB
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53 - Chapter 13. Basic matrix operations, Part 2.mp4
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MP4
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27.1 MB
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54 - Chapter 13. Computational limits of matrix multiplication.mp4
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MP4
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47.8 MB
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55 - Chapter 14. Dimension reduction of matrix data.mp4
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61.7 MB
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56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp4
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MP4
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39 MB
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57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp4
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MP4
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37.6 MB
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58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp4
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MP4
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64.7 MB
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59 - Chapter 14. Clustering 4D data in two dimensions.mp4
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MP4
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54.4 MB
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6 - Chapter 3. Running random simulations in NumPy.mp4
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MP4
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36.4 MB
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60 - Chapter 14. Limitations of PCA.mp4
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30.8 MB
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61 - Chapter 14. Computing principal components without rotation.mp4
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47.8 MB
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62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp4
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MP4
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44.7 MB
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63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp4
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MP4
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34.4 MB
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64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp4
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MP4
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68.6 MB
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65 - Chapter 15. NLP analysis of large text datasets.mp4
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MP4
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47.2 MB
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66 - Chapter 15. Vectorizing documents using scikit-learn.mp4
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MP4
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87.1 MB
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67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp4
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56.6 MB
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68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp4
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MP4
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48.1 MB
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69 - Chapter 15. Computing similarities across large document datasets.mp4
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MP4
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60.2 MB
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7 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp4
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MP4
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47.6 MB
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70 - Chapter 15. Clustering texts by topic, Part 1.mp4
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MP4
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73.3 MB
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71 - Chapter 15. Clustering texts by topic, Part 2.mp4
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MP4
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87.1 MB
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72 - Chapter 15. Visualizing text clusters.mp4
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MP4
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58.9 MB
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73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp4
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50.6 MB
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74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp4
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MP4
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58.8 MB
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75 - Chapter 16. Extracting text from web pages.mp4
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39.6 MB
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76 - Chapter 16. The structure of HTML documents.mp4
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MP4
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62.9 MB
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77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp4
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40.4 MB
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78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp4
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MP4
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46.8 MB
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79 - Chapter 17. Case study 4 solution.mp4
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MP4
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37.4 MB
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8 - Chapter 3. Deriving probabilities from histograms.mp4
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MP4
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57.6 MB
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80 - Chapter 17. Exploring the HTML for skill descriptions.mp4
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59.7 MB
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81 - Chapter 17. Filtering jobs by relevance.mp4
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73.2 MB
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82 - Chapter 17. Clustering skills in relevant job postings.mp4
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66.5 MB
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83 - Chapter 17. Investigating the technical skill clusters.mp4
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41.5 MB
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84 - Chapter 17. Exploring clusters at alternative values of K.mp4
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69.4 MB
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85 - Chapter 17. Analyzing the 700 most relevant postings.mp4
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MP4
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40.9 MB
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86 - Case study 5 - Predicting future friendships from social network data.mp4
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MP4
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80.4 MB
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87 - Chapter 18. An introduction to graph theory and network analysis.mp4
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74.9 MB
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88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp4
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MP4
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30.9 MB
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89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp4
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MP4
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53.1 MB
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9 - Chapter 3. Computing histograms in NumPy.mp4
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MP4
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53 MB
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90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp4
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MP4
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57.4 MB
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91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp4
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MP4
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32.1 MB
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92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp4
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MP4
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49 MB
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93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp4
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MP4
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75.1 MB
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94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp4
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MP4
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40.2 MB
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95 - Chapter 19. Deriving PageRank centrality from probability theory.mp4
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MP4
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48.4 MB
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96 - Chapter 19. Computing PageRank centrality using NetworkX.mp4
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MP4
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44.7 MB
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97 - Chapter 19. Community detection using Markov clustering, Part 1.mp4
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MP4
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60.1 MB
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98 - Chapter 19. Community detection using Markov clustering, Part 2.mp4
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MP4
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75.2 MB
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99 - Chapter 19. Uncovering friend groups in social networks.mp4
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MP4
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58 MB
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For $3, Get Anything Official like Windows 11 keys + Microsoft Office 365 Accounts! Hurry! Limited Time Offer.url
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URL
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1.8 KB
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How you can help our Group!.txt
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TXT
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204.8 B
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![O’REILLY | Data Science Bookcamp, Video Edition [FCO]](/static/img/no-torrent-image.png "O’REILLY | Data Science Bookcamp, Video Edition [FCO]")
