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O`REILLY - Data Science Bookcamp, VIDEO EDITION
magnet:?xt=urn:btih:401c42ce7802e2a942f9b9d94ab17ed09d14887f&dn=O`REILLY - Data Science Bookcamp, VIDEO EDITION
磁力链接详情
Hash值:
401c42ce7802e2a942f9b9d94ab17ed09d14887f
点击数:
176
文件大小:
6.44 GB
文件数量:
128
创建日期:
2022-11-22 17:30
最后访问:
2024-5-18 20:25
访问标签:
O`REILLY
-
Data
Science
Bookcamp
VIDEO
EDITION
文件列表详情
01 - Case study 1 - Finding the winning strategy in a card game.mp4 6.89 MB
02 - Chapter 1. Computing probabilities using Python This section covers.mp4 56.75 MB
03 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4 60.89 MB
04 - Chapter 2. Plotting probabilities using Matplotlib.mp4 53.74 MB
05 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4 65.57 MB
06 - Chapter 3. Running random simulations in NumPy.mp4 36.35 MB
07 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp4 47.59 MB
08 - Chapter 3. Deriving probabilities from histograms.mp4 57.63 MB
09 - Chapter 3. Computing histograms in NumPy.mp4 52.99 MB
10 - Chapter 3. Using permutations to shuffle cards.mp4 35.4 MB
11 - Chapter 4. Case study 1 solution.mp4 34.27 MB
12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4 47.1 MB
13 - Case study 2 - Assessing online ad clicks for significance.mp4 31.4 MB
14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4 76.23 MB
15 - Chapter 5. Mean as a measure of centrality.mp4 36.58 MB
16 - Chapter 5. Variance as a measure of dispersion.mp4 73.89 MB
17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4 58.61 MB
18 - Chapter 6. Comparing two sampled normal curves.mp4 31.46 MB
19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4 55.19 MB
20 - Chapter 6. Computing the area beneath a normal curve.mp4 64.57 MB
21 - Chapter 7. Statistical hypothesis testing.mp4 39.19 MB
22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4 68.3 MB
23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4 79.88 MB
24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4 53.28 MB
25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4 52.78 MB
26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp4 43.69 MB
27 - Chapter 8. Analyzing tables using Pandas.mp4 40.87 MB
28 - Chapter 8. Retrieving table rows.mp4 38.24 MB
29 - Chapter 8. Saving and loading table data.mp4 40.28 MB
30 - Chapter 9. Case study 2 solution.mp4 33.6 MB
31 - Chapter 9. Determining statistical significance.mp4 43.58 MB
32 - Case study 3 - Tracking disease outbreaks using news headlines.mp4 6.6 MB
33 - Chapter 10. Clustering data into groups.mp4 61.4 MB
34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4 61.2 MB
35 - Chapter 10. Using density to discover clusters.mp4 52.23 MB
36 - Chapter 10. Clustering based on non-Euclidean distance.mp4 68.79 MB
37 - Chapter 10. Analyzing clusters using Pandas.mp4 40.48 MB
38 - Chapter 11. Geographic location visualization and analysis.mp4 46.58 MB
39 - Chapter 11. Plotting maps using Cartopy.mp4 33.23 MB
40 - Chapter 11. Visualizing maps.mp4 58.27 MB
41 - Chapter 11. Location tracking using GeoNamesCache.mp4 62.35 MB
42 - Chapter 11. Limitations of the GeoNamesCache library.mp4 69.19 MB
43 - Chapter 12. Case study 3 solution.mp4 34.63 MB
44 - Chapter 12. Visualizing and clustering the extracted location data.mp4 70.72 MB
45 - Case study 4 - Using online job postings to improve your data science resume.mp4 23.95 MB
46 - Chapter 13. Measuring text similarities.mp4 36.28 MB
47 - Chapter 13. Simple text comparison.mp4 44 MB
48 - Chapter 13. Replacing words with numeric values.mp4 42.07 MB
49 - Chapter 13. Vectorizing texts using word counts.mp4 44.5 MB
50 - Chapter 13. Using normalization to improve TF vector similarity.mp4 48.56 MB
51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp4 41.64 MB
52 - Chapter 13. Basic matrix operations, Part 1.mp4 48.78 MB
53 - Chapter 13. Basic matrix operations, Part 2.mp4 27.15 MB
54 - Chapter 13. Computational limits of matrix multiplication.mp4 47.81 MB
55 - Chapter 14. Dimension reduction of matrix data.mp4 61.74 MB
56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp4 38.99 MB
57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp4 37.56 MB
58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp4 64.72 MB
59 - Chapter 14. Clustering 4D data in two dimensions.mp4 54.44 MB
60 - Chapter 14. Limitations of PCA.mp4 30.77 MB
61 - Chapter 14. Computing principal components without rotation.mp4 47.8 MB
62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp4 44.67 MB
63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp4 34.38 MB
64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp4 68.6 MB
65 - Chapter 15. NLP analysis of large text datasets.mp4 47.16 MB
66 - Chapter 15. Vectorizing documents using scikit-learn.mp4 87.06 MB
67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp4 56.59 MB
68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp4 48.13 MB
69 - Chapter 15. Computing similarities across large document datasets.mp4 60.24 MB
70 - Chapter 15. Clustering texts by topic, Part 1.mp4 73.3 MB
71 - Chapter 15. Clustering texts by topic, Part 2.mp4 87.08 MB
72 - Chapter 15. Visualizing text clusters.mp4 58.9 MB
73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp4 50.57 MB
74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp4 58.83 MB
75 - Chapter 16. Extracting text from web pages.mp4 39.55 MB
76 - Chapter 16. The structure of HTML documents.mp4 62.95 MB
77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp4 40.42 MB
78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp4 46.78 MB
79 - Chapter 17. Case study 4 solution.mp4 37.42 MB
80 - Chapter 17. Exploring the HTML for skill descriptions.mp4 59.65 MB
81 - Chapter 17. Filtering jobs by relevance.mp4 73.18 MB
82 - Chapter 17. Clustering skills in relevant job postings.mp4 66.54 MB
83 - Chapter 17. Investigating the technical skill clusters.mp4 41.46 MB
84 - Chapter 17. Exploring clusters at alternative values of K.mp4 69.37 MB
85 - Chapter 17. Analyzing the 700 most relevant postings.mp4 40.95 MB
86 - Case study 5 - Predicting future friendships from social network data.mp4 80.4 MB
87 - Chapter 18. An introduction to graph theory and network analysis.mp4 74.88 MB
88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp4 30.92 MB
89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp4 53.06 MB
90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp4 57.39 MB
91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp4 32.12 MB
92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp4 49.04 MB
93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp4 75.08 MB
94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp4 40.21 MB
95 - Chapter 19. Deriving PageRank centrality from probability theory.mp4 48.36 MB
96 - Chapter 19. Computing PageRank centrality using NetworkX.mp4 44.66 MB
97 - Chapter 19. Community detection using Markov clustering, Part 1.mp4 60.05 MB
98 - Chapter 19. Community detection using Markov clustering, Part 2.mp4 75.21 MB
99 - Chapter 19. Uncovering friend groups in social networks.mp4 57.99 MB
100 - Chapter 20. Network-driven supervised machine learning.mp4 48.95 MB
101 - Chapter 20. The basics of supervised machine learning.mp4 49.2 MB
102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4 37.28 MB
103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4 55.24 MB
104 - Chapter 20. Optimizing KNN performance.mp4 35.68 MB
105 - Chapter 20. Running a grid search using scikit-learn.mp4 39.33 MB
106 - Chapter 20. Limitations of the KNN algorithm.mp4 63.16 MB
107 - Chapter 21. Training linear classifiers with logistic regression.mp4 58.26 MB
108 - Chapter 21. Training a linear classifier, Part 1.mp4 43.52 MB
109 - Chapter 21. Training a linear classifier, Part 2.mp4 73.26 MB
110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4 43.42 MB
111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4 43.12 MB
112 - Chapter 21. Training linear classifiers using scikit-learn.mp4 49.64 MB
113 - Chapter 21. Measuring feature importance with coefficients.mp4 93.13 MB
114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4 65.2 MB
115 - Chapter 22. Training a nested if_else model using two features.mp4 53.25 MB
116 - Chapter 22. Deciding which feature to split on.mp4 57.23 MB
117 - Chapter 22. Training if_else models with more than two features.mp4 57.79 MB
118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4 51.86 MB
119 - Chapter 22. Studying cancerous cells using feature importance.mp4 59.29 MB
120 - Chapter 22. Improving performance using random forest classification.mp4 57.38 MB
121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4 52.96 MB
122 - Chapter 23. Case study 5 solution.mp4 32.94 MB
123 - Chapter 23. Exploring the experimental observations.mp4 38.99 MB
124 - Chapter 23. Training a predictive model using network features, Part 1.mp4 52.59 MB
125 - Chapter 23. Training a predictive model using network features, Part 2.mp4 53.87 MB
126 - Chapter 23. Adding profile features to the model.mp4 62.03 MB
127 - Chapter 23. Optimizing performance across a steady set of features.mp4 42.55 MB
128 - Chapter 23. Interpreting the trained model.mp4 64.17 MB
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