种子简介

种子名称： O`REILLY - Data Science Bookcamp, VIDEO EDITION
文件类型： 视频
文件数目： 128个文件
文件大小： 6.44 GB
收录时间： 2022-11-22 17:30
已经下载： 3次
资源热度： 232
最近下载： 2024-11-28 10:29

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:401c42ce7802e2a942f9b9d94ab17ed09d14887f&dn=O`REILLY - Data Science Bookcamp, VIDEO EDITION 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

O`REILLY - Data Science Bookcamp, VIDEO EDITION.torrent

01 - Case study 1 - Finding the winning strategy in a card game.mp46.89MB

02 - Chapter 1. Computing probabilities using Python This section covers.mp456.75MB

03 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp460.89MB

04 - Chapter 2. Plotting probabilities using Matplotlib.mp453.74MB

05 - Chapter 2. Comparing multiple coin-flip probability distributions.mp465.57MB

06 - Chapter 3. Running random simulations in NumPy.mp436.35MB

07 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp447.59MB

08 - Chapter 3. Deriving probabilities from histograms.mp457.63MB

09 - Chapter 3. Computing histograms in NumPy.mp452.99MB

10 - Chapter 3. Using permutations to shuffle cards.mp435.4MB

11 - Chapter 4. Case study 1 solution.mp434.27MB

12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp447.1MB

13 - Case study 2 - Assessing online ad clicks for significance.mp431.4MB

14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp476.23MB

15 - Chapter 5. Mean as a measure of centrality.mp436.58MB

16 - Chapter 5. Variance as a measure of dispersion.mp473.89MB

17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp458.61MB

18 - Chapter 6. Comparing two sampled normal curves.mp431.46MB

19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp455.19MB

20 - Chapter 6. Computing the area beneath a normal curve.mp464.57MB

21 - Chapter 7. Statistical hypothesis testing.mp439.19MB

22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp468.3MB

23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp479.88MB

24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp453.28MB

25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp452.78MB

26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp443.69MB

27 - Chapter 8. Analyzing tables using Pandas.mp440.87MB

28 - Chapter 8. Retrieving table rows.mp438.24MB

29 - Chapter 8. Saving and loading table data.mp440.28MB

30 - Chapter 9. Case study 2 solution.mp433.6MB

31 - Chapter 9. Determining statistical significance.mp443.58MB

32 - Case study 3 - Tracking disease outbreaks using news headlines.mp46.6MB

33 - Chapter 10. Clustering data into groups.mp461.4MB

34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp461.2MB

35 - Chapter 10. Using density to discover clusters.mp452.23MB

36 - Chapter 10. Clustering based on non-Euclidean distance.mp468.79MB

37 - Chapter 10. Analyzing clusters using Pandas.mp440.48MB

38 - Chapter 11. Geographic location visualization and analysis.mp446.58MB

39 - Chapter 11. Plotting maps using Cartopy.mp433.23MB

40 - Chapter 11. Visualizing maps.mp458.27MB

41 - Chapter 11. Location tracking using GeoNamesCache.mp462.35MB

42 - Chapter 11. Limitations of the GeoNamesCache library.mp469.19MB

43 - Chapter 12. Case study 3 solution.mp434.63MB

44 - Chapter 12. Visualizing and clustering the extracted location data.mp470.72MB

45 - Case study 4 - Using online job postings to improve your data science resume.mp423.95MB

46 - Chapter 13. Measuring text similarities.mp436.28MB

47 - Chapter 13. Simple text comparison.mp444MB

48 - Chapter 13. Replacing words with numeric values.mp442.07MB

49 - Chapter 13. Vectorizing texts using word counts.mp444.5MB

50 - Chapter 13. Using normalization to improve TF vector similarity.mp448.56MB

51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp441.64MB

52 - Chapter 13. Basic matrix operations, Part 1.mp448.78MB

53 - Chapter 13. Basic matrix operations, Part 2.mp427.15MB

54 - Chapter 13. Computational limits of matrix multiplication.mp447.81MB

55 - Chapter 14. Dimension reduction of matrix data.mp461.74MB

56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp438.99MB

57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp437.56MB

58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp464.72MB

59 - Chapter 14. Clustering 4D data in two dimensions.mp454.44MB

60 - Chapter 14. Limitations of PCA.mp430.77MB

61 - Chapter 14. Computing principal components without rotation.mp447.8MB

62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp444.67MB

63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp434.38MB

64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp468.6MB

65 - Chapter 15. NLP analysis of large text datasets.mp447.16MB

66 - Chapter 15. Vectorizing documents using scikit-learn.mp487.06MB

67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp456.59MB

68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp448.13MB

69 - Chapter 15. Computing similarities across large document datasets.mp460.24MB

70 - Chapter 15. Clustering texts by topic, Part 1.mp473.3MB

71 - Chapter 15. Clustering texts by topic, Part 2.mp487.08MB

72 - Chapter 15. Visualizing text clusters.mp458.9MB

73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp450.57MB

74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp458.83MB

75 - Chapter 16. Extracting text from web pages.mp439.55MB

76 - Chapter 16. The structure of HTML documents.mp462.95MB

77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp440.42MB

78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp446.78MB

79 - Chapter 17. Case study 4 solution.mp437.42MB

80 - Chapter 17. Exploring the HTML for skill descriptions.mp459.65MB

81 - Chapter 17. Filtering jobs by relevance.mp473.18MB

82 - Chapter 17. Clustering skills in relevant job postings.mp466.54MB

83 - Chapter 17. Investigating the technical skill clusters.mp441.46MB

84 - Chapter 17. Exploring clusters at alternative values of K.mp469.37MB

85 - Chapter 17. Analyzing the 700 most relevant postings.mp440.95MB

86 - Case study 5 - Predicting future friendships from social network data.mp480.4MB

87 - Chapter 18. An introduction to graph theory and network analysis.mp474.88MB

88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp430.92MB

89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp453.06MB

90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp457.39MB

91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp432.12MB

92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp449.04MB

93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp475.08MB

94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp440.21MB

95 - Chapter 19. Deriving PageRank centrality from probability theory.mp448.36MB

96 - Chapter 19. Computing PageRank centrality using NetworkX.mp444.66MB

97 - Chapter 19. Community detection using Markov clustering, Part 1.mp460.05MB

98 - Chapter 19. Community detection using Markov clustering, Part 2.mp475.21MB

99 - Chapter 19. Uncovering friend groups in social networks.mp457.99MB

100 - Chapter 20. Network-driven supervised machine learning.mp448.95MB

101 - Chapter 20. The basics of supervised machine learning.mp449.2MB

102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp437.28MB

103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp455.24MB

104 - Chapter 20. Optimizing KNN performance.mp435.68MB

105 - Chapter 20. Running a grid search using scikit-learn.mp439.33MB

106 - Chapter 20. Limitations of the KNN algorithm.mp463.16MB

107 - Chapter 21. Training linear classifiers with logistic regression.mp458.26MB

108 - Chapter 21. Training a linear classifier, Part 1.mp443.52MB

109 - Chapter 21. Training a linear classifier, Part 2.mp473.26MB

110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp443.42MB

111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp443.12MB

112 - Chapter 21. Training linear classifiers using scikit-learn.mp449.64MB

113 - Chapter 21. Measuring feature importance with coefficients.mp493.13MB

114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp465.2MB

115 - Chapter 22. Training a nested if_else model using two features.mp453.25MB

116 - Chapter 22. Deciding which feature to split on.mp457.23MB

117 - Chapter 22. Training if_else models with more than two features.mp457.79MB

118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp451.86MB

119 - Chapter 22. Studying cancerous cells using feature importance.mp459.29MB

120 - Chapter 22. Improving performance using random forest classification.mp457.38MB

121 - Chapter 22. Training random forest classifiers using scikit-learn.mp452.96MB

122 - Chapter 23. Case study 5 solution.mp432.94MB

123 - Chapter 23. Exploring the experimental observations.mp438.99MB

124 - Chapter 23. Training a predictive model using network features, Part 1.mp452.59MB

125 - Chapter 23. Training a predictive model using network features, Part 2.mp453.87MB

126 - Chapter 23. Adding profile features to the model.mp462.03MB

127 - Chapter 23. Optimizing performance across a steady set of features.mp442.55MB

128 - Chapter 23. Interpreting the trained model.mp464.17MB