首页 磁力链接怎么用

[FreeTutorials.Eu] Udemy - Machine Learning A-Z Become Kaggle Master

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 2019-2-16 19:07 2024-6-4 10:06 223 13.87 GB 257
磁力链接
magnet:?xt=urn:btih:4262230db3b95cedb1839b5e6dd665d05d43fe5d
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOjQyNjIyMzBkYjNiOTVjZWRiMTgzOWI1ZTZkZDY2NWQwNWQ0M2ZlNWRaWg==
二维码链接
[FreeTutorials.Eu] Udemy - Machine Learning A-Z Become Kaggle Master的二维码
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

FreeTutorials Eu Udemy - Machine Learning A-Z Become Kaggle Master

文件列表
  1. 10. Multiple Linear Regression/10. Case Study Part5.mp445.73MB
  2. 10. Multiple Linear Regression/11. Case Study Part6 (RFE).mp464.15MB
  3. 10. Multiple Linear Regression/1. Introduction.mp416.46MB
  4. 10. Multiple Linear Regression/2. Case Study part1.mp483.04MB
  5. 10. Multiple Linear Regression/3. Case Study part2.mp498.41MB
  6. 10. Multiple Linear Regression/4. Case Study part3.mp468.67MB
  7. 10. Multiple Linear Regression/5. Adjusted R Square.mp48.08MB
  8. 10. Multiple Linear Regression/6. Case Study Part1.mp468.55MB
  9. 10. Multiple Linear Regression/7. Case Study Part2.mp472.9MB
  10. 10. Multiple Linear Regression/8. Case Study Part3.mp466.56MB
  11. 10. Multiple Linear Regression/9. Case Study Part4.mp4132.2MB
  12. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/1. Introduction to the Problem Statement.mp440.85MB
  13. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/2. Playing With Data.mp481.36MB
  14. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/3. Building Model Part1.mp455.07MB
  15. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/4. Building Model Part2.mp487.8MB
  16. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/5. Building Model Part3.mp448.52MB
  17. 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/6. Verification of Model.mp439.49MB
  18. 12. Gradient Descent/1. Pre-Req For Gradient Descent Part1.mp461.24MB
  19. 12. Gradient Descent/2. Pre-Req For Gradient Descent Part2.mp432.9MB
  20. 12. Gradient Descent/3. Cost Functions.mp413.16MB
  21. 12. Gradient Descent/4. Defining Cost Functions More Formally.mp436.51MB
  22. 12. Gradient Descent/5. Gradient Descent.mp437.66MB
  23. 12. Gradient Descent/6. Optimisation.mp421.68MB
  24. 12. Gradient Descent/7. Closed Form Vs Gradient Descent.mp426.61MB
  25. 12. Gradient Descent/8. Gradient Descent case study.mp471.66MB
  26. 13. KNN/10. Case Study.mp470.71MB
  27. 13. KNN/11. Classification Case1.mp484.22MB
  28. 13. KNN/12. Classification Case2.mp452.23MB
  29. 13. KNN/13. Classification Case3.mp452.97MB
  30. 13. KNN/14. Classification Case4.mp441.1MB
  31. 13. KNN/1. Introduction to Classification.mp454.11MB
  32. 13. KNN/2. Defining Classification Mathematically.mp439.99MB
  33. 13. KNN/3. Introduction to KNN.mp447.13MB
  34. 13. KNN/4. Accuracy of KNN.mp457.16MB
  35. 13. KNN/5. Effectiveness of KNN.mp448.23MB
  36. 13. KNN/6. Distance Metrics.mp447.9MB
  37. 13. KNN/7. Distance Metrics Part2.mp428.83MB
  38. 13. KNN/8. Finding k.mp433.32MB
  39. 13. KNN/9. KNN on Regression.mp49.28MB
  40. 14. Model Performance Metrics/1. Performance Metrics Part1.mp4113.83MB
  41. 14. Model Performance Metrics/2. Performance Metrics Part2.mp490.48MB
  42. 14. Model Performance Metrics/3. Performance Metrics Part3.mp424.02MB
  43. 15. Model Selection Part1/1. Model Creation Case1.mp452.09MB
  44. 15. Model Selection Part1/2. Model Creation Case2.mp434.67MB
  45. 15. Model Selection Part1/3. Gridsearch Case study Part1.mp4124.24MB
  46. 15. Model Selection Part1/4. Gridsearch Case study Part2.mp4178.88MB
  47. 16. Naive Bayes/10. Case Study 2 Part1.mp474.57MB
  48. 16. Naive Bayes/11. Case Study 2 Part2.mp425.35MB
  49. 16. Naive Bayes/1. Introduction to Naive Bayes.mp473.37MB
  50. 16. Naive Bayes/2. Bayes Theorem.mp463.05MB
  51. 16. Naive Bayes/3. Practical Example from NB with One Column.mp480.59MB
  52. 16. Naive Bayes/4. Practical Example from NB with Multiple Columns.mp459.83MB
  53. 16. Naive Bayes/5. Naive Bayes On Text Data Part1.mp454.74MB
  54. 16. Naive Bayes/6. Naive Bayes On Text Data Part2.mp446.06MB
  55. 16. Naive Bayes/7. Laplace Smoothing.mp455.26MB
  56. 16. Naive Bayes/8. Bernoulli Naive Bayes.mp427.11MB
  57. 16. Naive Bayes/9. Case Study 1.mp495.46MB
  58. 17. Logistic Regression/1. Introduction.mp426.6MB
  59. 17. Logistic Regression/2. Sigmoid Function.mp444.31MB
  60. 17. Logistic Regression/3. Log Odds.mp441.83MB
  61. 17. Logistic Regression/4. Case Study.mp4198.2MB
  62. 18. Support Vector Machine (SVM)/10. Kernel Part2.mp471.13MB
  63. 18. Support Vector Machine (SVM)/11. Case Study 2.mp490MB
  64. 18. Support Vector Machine (SVM)/12. Case Study 3 Part1.mp456.01MB
  65. 18. Support Vector Machine (SVM)/13. Case Study 3 Part2.mp461.28MB
  66. 18. Support Vector Machine (SVM)/14. Case Study 4.mp4164.41MB
  67. 18. Support Vector Machine (SVM)/1. Introduction.mp458.71MB
  68. 18. Support Vector Machine (SVM)/2. Hyperplane Part1.mp427.07MB
  69. 18. Support Vector Machine (SVM)/3. Hyperplane Part2.mp465.32MB
  70. 18. Support Vector Machine (SVM)/4. Maths Behind SVM.mp424.04MB
  71. 18. Support Vector Machine (SVM)/5. Support Vectors.mp411.04MB
  72. 18. Support Vector Machine (SVM)/6. Slack Variable.mp433.27MB
  73. 18. Support Vector Machine (SVM)/7. SVM Case Study Part1.mp474.15MB
  74. 18. Support Vector Machine (SVM)/8. SVM Case Study Part2.mp466.16MB
  75. 18. Support Vector Machine (SVM)/9. Kernel Part1.mp449.24MB
  76. 19. Decision Tree/10. DT Case Study Part2.mp495.71MB
  77. 19. Decision Tree/1. Introduction.mp429.78MB
  78. 19. Decision Tree/2. Example of DT.mp440.59MB
  79. 19. Decision Tree/3. Homogenity.mp420.61MB
  80. 19. Decision Tree/4. Gini Index.mp444.19MB
  81. 19. Decision Tree/5. Information Gain Part1.mp429.29MB
  82. 19. Decision Tree/6. Information Gain Part2.mp427.37MB
  83. 19. Decision Tree/7. Advantages and Disadvantages of DT.mp415.45MB
  84. 19. Decision Tree/8. Preventing Overfitting Issues in DT.mp440.29MB
  85. 19. Decision Tree/9. DT Case Study Part1.mp4125.45MB
  86. 1. Python Fundamentals/10. Functions.mp485.62MB
  87. 1. Python Fundamentals/11. String Part1.mp4106.01MB
  88. 1. Python Fundamentals/12. String Part2.mp427.38MB
  89. 1. Python Fundamentals/13. List Part1.mp410.04MB
  90. 1. Python Fundamentals/14. List Part2.mp487.32MB
  91. 1. Python Fundamentals/15. List Part3.mp473.56MB
  92. 1. Python Fundamentals/16. List Part4.mp463.85MB
  93. 1. Python Fundamentals/17. Tuples.mp467.33MB
  94. 1. Python Fundamentals/18. Sets.mp458.16MB
  95. 1. Python Fundamentals/19. Dictionaries.mp461.6MB
  96. 1. Python Fundamentals/1. Introduction to the course.mp493.85MB
  97. 1. Python Fundamentals/20. Comprehentions.mp470.54MB
  98. 1. Python Fundamentals/2. Introduction to Kaggle.mp490.07MB
  99. 1. Python Fundamentals/3. Installation of Python and Anaconda.mp482.29MB
  100. 1. Python Fundamentals/4. Python Introduction.mp410.25MB
  101. 1. Python Fundamentals/5. Variables in Python.mp4110.46MB
  102. 1. Python Fundamentals/6. Numeric Operations in Python.mp436.92MB
  103. 1. Python Fundamentals/7. Logical Operations.mp417.32MB
  104. 1. Python Fundamentals/8. If else Loop.mp464.01MB
  105. 1. Python Fundamentals/9. for while Loop.mp477.78MB
  106. 20. Ensembling/10. Adaboost Part2.mp438.46MB
  107. 20. Ensembling/11. Adaboost Case Study.mp453.65MB
  108. 20. Ensembling/12. XGBoost.mp423.11MB
  109. 20. Ensembling/13. Boosting Part1.mp413.69MB
  110. 20. Ensembling/14. Boosting Part2.mp435.51MB
  111. 20. Ensembling/15. XGboost Algorithm.mp438.76MB
  112. 20. Ensembling/16. Case Study Part1.mp4141.54MB
  113. 20. Ensembling/17. Case Study Part2.mp4136.7MB
  114. 20. Ensembling/18. Case Study Part3.mp475.43MB
  115. 20. Ensembling/1. Introduction to Ensembles.mp439.28MB
  116. 20. Ensembling/2. Bagging.mp471.21MB
  117. 20. Ensembling/3. Advantages.mp414.87MB
  118. 20. Ensembling/4. Runtime.mp416.38MB
  119. 20. Ensembling/5. Case study.mp473.09MB
  120. 20. Ensembling/6. Introduction to Boosting.mp433.05MB
  121. 20. Ensembling/7. Weak Learners.mp417.9MB
  122. 20. Ensembling/8. Shallow Decision Tree.mp414.96MB
  123. 20. Ensembling/9. Adaboost Part1.mp441.53MB
  124. 21. Model Selection Part2/1. Model Selection Part1.mp4104.3MB
  125. 21. Model Selection Part2/2. Model Selection Part2.mp441.33MB
  126. 21. Model Selection Part2/3. Model Selection Part3.mp435.66MB
  127. 22. Unsupervised Learning/10. Case Study Part2.mp461.33MB
  128. 22. Unsupervised Learning/11. More on Segmentation.mp418.06MB
  129. 22. Unsupervised Learning/12. Hierarchial Clustering.mp438.02MB
  130. 22. Unsupervised Learning/13. Case Study.mp434.4MB
  131. 22. Unsupervised Learning/1. Introduction to Clustering.mp459.13MB
  132. 22. Unsupervised Learning/2. Segmentation.mp428.65MB
  133. 22. Unsupervised Learning/3. Kmeans.mp457.71MB
  134. 22. Unsupervised Learning/4. Maths Behind Kmeans.mp453.75MB
  135. 22. Unsupervised Learning/5. More Maths.mp49.43MB
  136. 22. Unsupervised Learning/6. Kmeans plus.mp451.78MB
  137. 22. Unsupervised Learning/7. Value of K.mp435.82MB
  138. 22. Unsupervised Learning/8. Hopkins test.mp412.27MB
  139. 22. Unsupervised Learning/9. Case Study Part1.mp495.82MB
  140. 23. Dimension Reduction/1. Introduction.mp4156.68MB
  141. 23. Dimension Reduction/2. PCA.mp498.39MB
  142. 23. Dimension Reduction/3. Maths Behind PCA.mp496.82MB
  143. 23. Dimension Reduction/4. Case Study Part1.mp445.47MB
  144. 23. Dimension Reduction/5. Case Study Part2.mp4123.06MB
  145. 24. Advanced Machine Learning Algorithms/10. Adjusted R Square.mp420.13MB
  146. 24. Advanced Machine Learning Algorithms/1. Introduction.mp430.94MB
  147. 24. Advanced Machine Learning Algorithms/2. Example Part1.mp427.48MB
  148. 24. Advanced Machine Learning Algorithms/3. Example Part2.mp445.11MB
  149. 24. Advanced Machine Learning Algorithms/4. Optimal Solution.mp465.23MB
  150. 24. Advanced Machine Learning Algorithms/5. Case study.mp439.97MB
  151. 24. Advanced Machine Learning Algorithms/6. Regularization.mp448.6MB
  152. 24. Advanced Machine Learning Algorithms/7. Ridge and Lasso.mp439.95MB
  153. 24. Advanced Machine Learning Algorithms/8. Case Study.mp4106.22MB
  154. 24. Advanced Machine Learning Algorithms/9. Model Selection.mp431.3MB
  155. 25. Deep Learning/1. Expectations.mp49.36MB
  156. 25. Deep Learning/2. Introduction.mp448.76MB
  157. 25. Deep Learning/3. History.mp461.86MB
  158. 25. Deep Learning/4. Perceptron.mp429.78MB
  159. 25. Deep Learning/5. Multi Layered Perceptron.mp463.83MB
  160. 25. Deep Learning/6. Neural Network Playground.mp4103.7MB
  161. 26. Project Kaggle/10. Response encoding and one hot encoder.mp454.68MB
  162. 26. Project Kaggle/11. Laplace Smoothing and Calibrated classifier.mp448.25MB
  163. 26. Project Kaggle/12. Significance of first categorical column.mp471.74MB
  164. 26. Project Kaggle/13. Second Categorical column.mp445.7MB
  165. 26. Project Kaggle/14. Third Categorical column.mp466.72MB
  166. 26. Project Kaggle/15. Data pre-processing before building machine learning model.mp450.59MB
  167. 26. Project Kaggle/16. Building Machine Learning model part1.mp4124.01MB
  168. 26. Project Kaggle/17. Building Machine Learning model part2.mp4135.18MB
  169. 26. Project Kaggle/18. Building Machine Learning model part3.mp438.41MB
  170. 26. Project Kaggle/19. Building Machine Learning model part4.mp433.07MB
  171. 26. Project Kaggle/1. Introduction to the Problem Statement.mp493.36MB
  172. 26. Project Kaggle/20. Building Machine Learning model part5.mp441.94MB
  173. 26. Project Kaggle/21. Building Machine Learning model part6.mp450.82MB
  174. 26. Project Kaggle/2. Playing With The Data.mp4137.05MB
  175. 26. Project Kaggle/3. Translating the Problem In Machine Learning World.mp4113.02MB
  176. 26. Project Kaggle/4. Dealing with Text Data.mp498.05MB
  177. 26. Project Kaggle/5. Train, Test And Cross Validation Split.mp4116.21MB
  178. 26. Project Kaggle/6. Understanding Evaluation Matrix Log Loss.mp485.5MB
  179. 26. Project Kaggle/7. Building A Worst Model.mp468.49MB
  180. 26. Project Kaggle/8. Evaluating Worst ML Model.mp458.87MB
  181. 26. Project Kaggle/9. First Categorical column analysis.mp471.13MB
  182. 2. Numpy/1. Introduction.mp424.74MB
  183. 2. Numpy/2. Numpy Operations Part1.mp4128.75MB
  184. 2. Numpy/3. Numpy Operations Part2.mp4169.97MB
  185. 3. Pandas/10. groupby.mp446.92MB
  186. 3. Pandas/11. Merging Part2.mp433.9MB
  187. 3. Pandas/12. Pivot Table.mp427.7MB
  188. 3. Pandas/1. Introduction.mp439.1MB
  189. 3. Pandas/2. Series.mp461.49MB
  190. 3. Pandas/3. DataFrame.mp466.19MB
  191. 3. Pandas/4. Operations Part1.mp412.02MB
  192. 3. Pandas/5. Operations Part2.mp444.1MB
  193. 3. Pandas/6. Indexes.mp450.11MB
  194. 3. Pandas/7. loc and iloc.mp459.37MB
  195. 3. Pandas/8. Reading CSV.mp442.47MB
  196. 3. Pandas/9. Merging Part1.mp430.01MB
  197. 4. Some Fun With Maths/1. Linear Algebra Vectors.mp4162.41MB
  198. 4. Some Fun With Maths/2. Linear Algebra Matrix Part1.mp495.26MB
  199. 4. Some Fun With Maths/3. Linear Algebra Matrix Part2.mp477.99MB
  200. 4. Some Fun With Maths/4. Linear Algebra Going From 2D to nD Part1.mp427.71MB
  201. 4. Some Fun With Maths/5. Linear Algebra 2D to nD Part2.mp425.78MB
  202. 5. Inferential Statistics/10. Normal Distribution.mp419.02MB
  203. 5. Inferential Statistics/11. z Score.mp423.8MB
  204. 5. Inferential Statistics/12. Sampling.mp438.73MB
  205. 5. Inferential Statistics/13. Sampling Distribution.mp425.51MB
  206. 5. Inferential Statistics/14. Central Limit Theorem.mp413.1MB
  207. 5. Inferential Statistics/15. Confidence Interval Part1.mp434.55MB
  208. 5. Inferential Statistics/16. Confidence Interval Part2.mp413.39MB
  209. 5. Inferential Statistics/1. Inferential Statistics.mp410.31MB
  210. 5. Inferential Statistics/2. Probability Theory.mp454.79MB
  211. 5. Inferential Statistics/3. Probability Distribution.mp424.24MB
  212. 5. Inferential Statistics/4. Expected Values Part1.mp424.25MB
  213. 5. Inferential Statistics/5. Expected Values Part2.mp414.49MB
  214. 5. Inferential Statistics/6. Without Experiment.mp428.68MB
  215. 5. Inferential Statistics/7. Binomial Distribution.mp417.58MB
  216. 5. Inferential Statistics/8. Commulative Distribution.mp48.37MB
  217. 5. Inferential Statistics/9. PDF.mp421MB
  218. 6. Hypothesis Testing/10. Types of Error.mp415.3MB
  219. 6. Hypothesis Testing/11. t- distribution Part1.mp421.31MB
  220. 6. Hypothesis Testing/12. t- distribution Part2.mp429.32MB
  221. 6. Hypothesis Testing/1. Introduction.mp431.09MB
  222. 6. Hypothesis Testing/2. NULL And Alternate Hypothesis.mp428.79MB
  223. 6. Hypothesis Testing/3. Examples.mp427.75MB
  224. 6. Hypothesis Testing/4. OneTwo Tailed Tests.mp438MB
  225. 6. Hypothesis Testing/5. Critical Value Method.mp424.71MB
  226. 6. Hypothesis Testing/6. z Table.mp458.63MB
  227. 6. Hypothesis Testing/7. Examples.mp426.42MB
  228. 6. Hypothesis Testing/8. More Examples.mp416.47MB
  229. 6. Hypothesis Testing/9. p Value.mp433.48MB
  230. 7. Data Visualisation/1. Matplotlib.mp4172.76MB
  231. 7. Data Visualisation/2. Seaborn.mp4184.74MB
  232. 7. Data Visualisation/3. Case Study.mp4113.2MB
  233. 7. Data Visualisation/4. Seaborn On Time Series Data.mp454.06MB
  234. 8. Exploratory Data Analysis/10. Univariate Analysis Part1.mp482.78MB
  235. 8. Exploratory Data Analysis/11. Univariate Analysis Part2.mp460.85MB
  236. 8. Exploratory Data Analysis/12. Segmented Analysis.mp424.47MB
  237. 8. Exploratory Data Analysis/13. Bivariate Analysis.mp460.6MB
  238. 8. Exploratory Data Analysis/14. Derived Columns.mp441.89MB
  239. 8. Exploratory Data Analysis/1. Introduction.mp43.79MB
  240. 8. Exploratory Data Analysis/2. Data Sourcing and Cleaning part1.mp415.56MB
  241. 8. Exploratory Data Analysis/3. Data Sourcing and Cleaning part2.mp415.62MB
  242. 8. Exploratory Data Analysis/4. Data Sourcing and Cleaning part3.mp410.03MB
  243. 8. Exploratory Data Analysis/5. Data Sourcing and Cleaning part4.mp410.37MB
  244. 8. Exploratory Data Analysis/6. Data Sourcing and Cleaning part5.mp412.41MB
  245. 8. Exploratory Data Analysis/7. Data Sourcing and Cleaning part6.mp453.7MB
  246. 8. Exploratory Data Analysis/8. Data Cleaning part1.mp476.24MB
  247. 8. Exploratory Data Analysis/9. Data Cleaning part2.mp429.7MB
  248. 9. Simple Linear Regression/10. Residual Square Error (RSE).mp44.55MB
  249. 9. Simple Linear Regression/1. Introduction to Machine Learning.mp411.16MB
  250. 9. Simple Linear Regression/2. Types of Machine Learning.mp435.38MB
  251. 9. Simple Linear Regression/3. Introduction to Linear Regression (LR).mp417.88MB
  252. 9. Simple Linear Regression/4. How LR Works.mp458.68MB
  253. 9. Simple Linear Regression/5. Some Fun With Maths Behind LR.mp452.75MB
  254. 9. Simple Linear Regression/6. R Square.mp452.47MB
  255. 9. Simple Linear Regression/7. LR Case Study Part1.mp4137.5MB
  256. 9. Simple Linear Regression/8. LR Case Study Part2.mp453.38MB
  257. 9. Simple Linear Regression/9. LR Case Study Part3.mp446.44MB
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
838888磁力链接 磁力链接bt种子搜索引擎磁力搜索 DMCA