首页
磁力链接怎么用
한국어
English
日本語
简体中文
繁體中文
[FreeCoursesOnline.Me] O`REILLY - Data Science Bookcamp, video edition
文件类型
收录时间
最后活跃
资源热度
文件大小
文件数量
视频
2023-12-29 02:19
2024-8-20 03:20
108
6.44 GB
128
磁力链接
magnet:?xt=urn:btih:68f1b06f13a8d2daa4491ee1d44b6731ac249612
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOjY4ZjFiMDZmMTNhOGQyZGFhNDQ5MWVlMWQ0NGI2NzMxYWMyNDk2MTJaWg==
二维码链接
种子下载(838888不存储任何种子文件)
种子下载线路1(迅雷)--推荐
种子下载线路2(比特彗星)
种子下载线路3(torcache)
3条线路均为国内外知名下载网站种子链接,内容跟本站无关!
相关链接
FreeCoursesOnline
Me
O`REILLY
-
Data
Science
Bookcamp
video
edition
文件列表
1 - Case study 1 - Finding the winning strategy in a card game.mp4
6.89MB
10 - Chapter 3. Using permutations to shuffle cards.mp4
35.4MB
100 - Chapter 20. Network-driven supervised machine learning.mp4
48.95MB
101 - Chapter 20. The basics of supervised machine learning.mp4
49.2MB
102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4
37.28MB
103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4
55.24MB
104 - Chapter 20. Optimizing KNN performance.mp4
35.68MB
105 - Chapter 20. Running a grid search using scikit-learn.mp4
39.33MB
106 - Chapter 20. Limitations of the KNN algorithm.mp4
63.16MB
107 - Chapter 21. Training linear classifiers with logistic regression.mp4
58.26MB
108 - Chapter 21. Training a linear classifier, Part 1.mp4
43.52MB
109 - Chapter 21. Training a linear classifier, Part 2.mp4
73.26MB
11 - Chapter 4. Case study 1 solution.mp4
34.27MB
110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4
43.42MB
111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4
43.12MB
112 - Chapter 21. Training linear classifiers using scikit-learn.mp4
49.64MB
113 - Chapter 21. Measuring feature importance with coefficients.mp4
93.13MB
114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4
65.2MB
115 - Chapter 22. Training a nested if_else model using two features.mp4
53.25MB
116 - Chapter 22. Deciding which feature to split on.mp4
57.23MB
117 - Chapter 22. Training if_else models with more than two features.mp4
57.79MB
118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4
51.86MB
119 - Chapter 22. Studying cancerous cells using feature importance.mp4
59.29MB
12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4
47.1MB
120 - Chapter 22. Improving performance using random forest classification.mp4
57.38MB
121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4
52.96MB
122 - Chapter 23. Case study 5 solution.mp4
32.94MB
123 - Chapter 23. Exploring the experimental observations.mp4
38.99MB
124 - Chapter 23. Training a predictive model using network features, Part 1.mp4
52.59MB
125 - Chapter 23. Training a predictive model using network features, Part 2.mp4
53.87MB
126 - Chapter 23. Adding profile features to the model.mp4
62.03MB
127 - Chapter 23. Optimizing performance across a steady set of features.mp4
42.55MB
128 - Chapter 23. Interpreting the trained model.mp4
64.17MB
13 - Case study 2 - Assessing online ad clicks for significance.mp4
31.4MB
14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4
76.23MB
15 - Chapter 5. Mean as a measure of centrality.mp4
36.58MB
16 - Chapter 5. Variance as a measure of dispersion.mp4
73.89MB
17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4
58.61MB
18 - Chapter 6. Comparing two sampled normal curves.mp4
31.46MB
19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4
55.19MB
2 - Chapter 1. Computing probabilities using Python This section covers.mp4
56.75MB
20 - Chapter 6. Computing the area beneath a normal curve.mp4
64.57MB
21 - Chapter 7. Statistical hypothesis testing.mp4
39.19MB
22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4
68.3MB
23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4
79.88MB
24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4
53.28MB
25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4
52.78MB
26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp4
43.69MB
27 - Chapter 8. Analyzing tables using Pandas.mp4
40.87MB
28 - Chapter 8. Retrieving table rows.mp4
38.24MB
29 - Chapter 8. Saving and loading table data.mp4
40.28MB
3 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4
60.89MB
30 - Chapter 9. Case study 2 solution.mp4
33.6MB
31 - Chapter 9. Determining statistical significance.mp4
43.58MB
32 - Case study 3 - Tracking disease outbreaks using news headlines.mp4
6.6MB
33 - Chapter 10. Clustering data into groups.mp4
61.4MB
34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4
61.2MB
35 - Chapter 10. Using density to discover clusters.mp4
52.23MB
36 - Chapter 10. Clustering based on non-Euclidean distance.mp4
68.79MB
37 - Chapter 10. Analyzing clusters using Pandas.mp4
40.48MB
38 - Chapter 11. Geographic location visualization and analysis.mp4
46.58MB
39 - Chapter 11. Plotting maps using Cartopy.mp4
33.23MB
4 - Chapter 2. Plotting probabilities using Matplotlib.mp4
53.74MB
40 - Chapter 11. Visualizing maps.mp4
58.27MB
41 - Chapter 11. Location tracking using GeoNamesCache.mp4
62.35MB
42 - Chapter 11. Limitations of the GeoNamesCache library.mp4
69.19MB
43 - Chapter 12. Case study 3 solution.mp4
34.63MB
44 - Chapter 12. Visualizing and clustering the extracted location data.mp4
70.72MB
45 - Case study 4 - Using online job postings to improve your data science resume.mp4
23.95MB
46 - Chapter 13. Measuring text similarities.mp4
36.28MB
47 - Chapter 13. Simple text comparison.mp4
44MB
48 - Chapter 13. Replacing words with numeric values.mp4
42.07MB
49 - Chapter 13. Vectorizing texts using word counts.mp4
44.5MB
5 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4
65.57MB
50 - Chapter 13. Using normalization to improve TF vector similarity.mp4
48.56MB
51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp4
41.64MB
52 - Chapter 13. Basic matrix operations, Part 1.mp4
48.78MB
53 - Chapter 13. Basic matrix operations, Part 2.mp4
27.15MB
54 - Chapter 13. Computational limits of matrix multiplication.mp4
47.81MB
55 - Chapter 14. Dimension reduction of matrix data.mp4
61.74MB
56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp4
38.99MB
57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp4
37.56MB
58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp4
64.72MB
59 - Chapter 14. Clustering 4D data in two dimensions.mp4
54.44MB
6 - Chapter 3. Running random simulations in NumPy.mp4
36.35MB
60 - Chapter 14. Limitations of PCA.mp4
30.77MB
61 - Chapter 14. Computing principal components without rotation.mp4
47.8MB
62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp4
44.67MB
63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp4
34.38MB
64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp4
68.6MB
65 - Chapter 15. NLP analysis of large text datasets.mp4
47.16MB
66 - Chapter 15. Vectorizing documents using scikit-learn.mp4
87.06MB
67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp4
56.59MB
68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp4
48.13MB
69 - Chapter 15. Computing similarities across large document datasets.mp4
60.24MB
7 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp4
47.59MB
70 - Chapter 15. Clustering texts by topic, Part 1.mp4
73.3MB
71 - Chapter 15. Clustering texts by topic, Part 2.mp4
87.08MB
72 - Chapter 15. Visualizing text clusters.mp4
58.9MB
73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp4
50.57MB
74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp4
58.83MB
75 - Chapter 16. Extracting text from web pages.mp4
39.55MB
76 - Chapter 16. The structure of HTML documents.mp4
62.95MB
77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp4
40.42MB
78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp4
46.78MB
79 - Chapter 17. Case study 4 solution.mp4
37.42MB
8 - Chapter 3. Deriving probabilities from histograms.mp4
57.63MB
80 - Chapter 17. Exploring the HTML for skill descriptions.mp4
59.65MB
81 - Chapter 17. Filtering jobs by relevance.mp4
73.18MB
82 - Chapter 17. Clustering skills in relevant job postings.mp4
66.54MB
83 - Chapter 17. Investigating the technical skill clusters.mp4
41.46MB
84 - Chapter 17. Exploring clusters at alternative values of K.mp4
69.37MB
85 - Chapter 17. Analyzing the 700 most relevant postings.mp4
40.95MB
86 - Case study 5 - Predicting future friendships from social network data.mp4
80.4MB
87 - Chapter 18. An introduction to graph theory and network analysis.mp4
74.88MB
88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp4
30.92MB
89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp4
53.06MB
9 - Chapter 3. Computing histograms in NumPy.mp4
52.99MB
90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp4
57.39MB
91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp4
32.12MB
92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp4
49.04MB
93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp4
75.08MB
94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp4
40.21MB
95 - Chapter 19. Deriving PageRank centrality from probability theory.mp4
48.36MB
96 - Chapter 19. Computing PageRank centrality using NetworkX.mp4
44.66MB
97 - Chapter 19. Community detection using Markov clustering, Part 1.mp4
60.05MB
98 - Chapter 19. Community detection using Markov clustering, Part 2.mp4
75.21MB
99 - Chapter 19. Uncovering friend groups in social networks.mp4
57.99MB
友情提示
不会用的朋友看这里 把磁力链接复制到离线下载,或者bt下载软件里即可下载文件,或者直接复制迅雷链接到迅雷里下载! 亲,你造吗?将网页分享给您的基友,下载的人越多速度越快哦!
违规内容投诉邮箱:
[email protected]
概述 838888磁力搜索是一个磁力链接搜索引擎,是学术研究的副产品,用于解决资源过度分散的问题 它通过BitTorrent协议加入DHT网络,实时的自动采集数据,仅存储文件的标题、大小、文件列表、文件标识符(磁力链接)等基础信息 838888磁力搜索不下载任何真实资源,无法判断资源的合法性及真实性,使用838888磁力搜索服务的用户需自行鉴别内容的真伪 838888磁力搜索不上传任何资源,不提供Tracker服务,不提供种子文件的下载,这意味着838888磁力搜索 838888磁力搜索是一个完全合法的系统