首页 磁力链接怎么用

[FreeCourseSite.com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science

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 2019-4-25 01:36 2024-12-25 17:14 210 5.65 GB 262
磁力链接
magnet:?xt=urn:btih:a8c5248a22c4a1e7319f46ebde756e70bf5afd05
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOmE4YzUyNDhhMjJjNGExZTczMTlmNDZlYmRlNzU2ZTcwYmY1YWZkMDVaWg==
二维码链接
[FreeCourseSite.com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science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

FreeCourseSite com Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science

文件列表
  1. 1. Welcome to the course!/1. Applications of Machine Learning.mp47.99MB
  2. 1. Welcome to the course!/2. Why Machine Learning is the Future.mp412.82MB
  3. 1. Welcome to the course!/5. Updates on Udemy Reviews.mp452.92MB
  4. 1. Welcome to the course!/6. Installing Python and Anaconda (Mac, Linux & Windows).mp419.52MB
  5. 1. Welcome to the course!/8. Installing R and R Studio (Mac, Linux & Windows).mp417.55MB
  6. 10. Evaluating Regression Models Performance/1. R-Squared Intuition.mp48.86MB
  7. 10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp419.29MB
  8. 10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.mp421.89MB
  9. 10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.mp424.21MB
  10. 12. Logistic Regression/1. Logistic Regression Intuition.mp429.18MB
  11. 12. Logistic Regression/10. Logistic Regression in R - Step 2.mp47.84MB
  12. 12. Logistic Regression/11. Logistic Regression in R - Step 3.mp414.59MB
  13. 12. Logistic Regression/12. Logistic Regression in R - Step 4.mp46.9MB
  14. 12. Logistic Regression/13. Logistic Regression in R - Step 5.mp451.68MB
  15. 12. Logistic Regression/14. R Classification Template.mp412.47MB
  16. 12. Logistic Regression/2. How to get the dataset.mp411.71MB
  17. 12. Logistic Regression/3. Logistic Regression in Python - Step 1.mp412.93MB
  18. 12. Logistic Regression/4. Logistic Regression in Python - Step 2.mp48.23MB
  19. 12. Logistic Regression/5. Logistic Regression in Python - Step 3.mp45.97MB
  20. 12. Logistic Regression/6. Logistic Regression in Python - Step 4.mp410.37MB
  21. 12. Logistic Regression/7. Logistic Regression in Python - Step 5.mp442.56MB
  22. 12. Logistic Regression/8. Python Classification Template.mp412.07MB
  23. 12. Logistic Regression/9. Logistic Regression in R - Step 1.mp412.58MB
  24. 13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp49.27MB
  25. 13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.mp411.71MB
  26. 13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp435.22MB
  27. 13. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp441.38MB
  28. 14. Support Vector Machine (SVM)/1. SVM Intuition.mp418.01MB
  29. 14. Support Vector Machine (SVM)/2. How to get the dataset.mp411.72MB
  30. 14. Support Vector Machine (SVM)/3. SVM in Python.mp431.17MB
  31. 14. Support Vector Machine (SVM)/4. SVM in R.mp432.27MB
  32. 15. Kernel SVM/1. Kernel SVM Intuition.mp45.79MB
  33. 15. Kernel SVM/2. Mapping to a higher dimension.mp413.74MB
  34. 15. Kernel SVM/3. The Kernel Trick.mp429.28MB
  35. 15. Kernel SVM/4. Types of Kernel Functions.mp412.3MB
  36. 15. Kernel SVM/5. How to get the dataset.mp411.71MB
  37. 15. Kernel SVM/6. Kernel SVM in Python.mp441.63MB
  38. 15. Kernel SVM/7. Kernel SVM in R.mp440.45MB
  39. 16. Naive Bayes/1. Bayes Theorem.mp443.9MB
  40. 16. Naive Bayes/2. Naive Bayes Intuition.mp427.79MB
  41. 16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp413.28MB
  42. 16. Naive Bayes/4. Naive Bayes Intuition (Extras).mp418.94MB
  43. 16. Naive Bayes/5. How to get the dataset.mp411.71MB
  44. 16. Naive Bayes/6. Naive Bayes in Python.mp423.39MB
  45. 16. Naive Bayes/7. Naive Bayes in R.mp437.32MB
  46. 17. Decision Tree Classification/1. Decision Tree Classification Intuition.mp418.8MB
  47. 17. Decision Tree Classification/2. How to get the dataset.mp411.71MB
  48. 17. Decision Tree Classification/3. Decision Tree Classification in Python.mp429.8MB
  49. 17. Decision Tree Classification/4. Decision Tree Classification in R.mp451.18MB
  50. 18. Random Forest Classification/1. Random Forest Classification Intuition.mp419.44MB
  51. 18. Random Forest Classification/2. How to get the dataset.mp411.72MB
  52. 18. Random Forest Classification/3. Random Forest Classification in Python.mp447.16MB
  53. 18. Random Forest Classification/4. Random Forest Classification in R.mp449.39MB
  54. 19. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp413.65MB
  55. 19. Evaluating Classification Models Performance/2. Confusion Matrix.mp48.21MB
  56. 19. Evaluating Classification Models Performance/3. Accuracy Paradox.mp43.8MB
  57. 19. Evaluating Classification Models Performance/4. CAP Curve.mp418.68MB
  58. 19. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp411.51MB
  59. 2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.mp42.98MB
  60. 2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.mp434.62MB
  61. 2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.mp419.67MB
  62. 2. -------------------- Part 1 Data Preprocessing --------------------/2. Get the dataset.mp421.15MB
  63. 2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.mp411.07MB
  64. 2. -------------------- Part 1 Data Preprocessing --------------------/4. Importing the Dataset.mp423.31MB
  65. 2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.mp432.16MB
  66. 2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.mp440.8MB
  67. 2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.mp439.04MB
  68. 21. K-Means Clustering/1. K-Means Clustering Intuition.mp426.87MB
  69. 21. K-Means Clustering/2. K-Means Random Initialization Trap.mp415.37MB
  70. 21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp423.13MB
  71. 21. K-Means Clustering/4. How to get the dataset.mp411.71MB
  72. 21. K-Means Clustering/5. K-Means Clustering in Python.mp439.77MB
  73. 21. K-Means Clustering/6. K-Means Clustering in R.mp429MB
  74. 22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.mp416.53MB
  75. 22. Hierarchical Clustering/10. HC in R - Step 1.mp47.38MB
  76. 22. Hierarchical Clustering/11. HC in R - Step 2.mp411.15MB
  77. 22. Hierarchical Clustering/12. HC in R - Step 3.mp47.81MB
  78. 22. Hierarchical Clustering/13. HC in R - Step 4.mp47.44MB
  79. 22. Hierarchical Clustering/14. HC in R - Step 5.mp46.88MB
  80. 22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.mp417.46MB
  81. 22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.mp422.82MB
  82. 22. Hierarchical Clustering/4. How to get the dataset.mp411.71MB
  83. 22. Hierarchical Clustering/5. HC in Python - Step 1.mp410.73MB
  84. 22. Hierarchical Clustering/6. HC in Python - Step 2.mp412.65MB
  85. 22. Hierarchical Clustering/7. HC in Python - Step 3.mp412.31MB
  86. 22. Hierarchical Clustering/8. HC in Python - Step 4.mp412.02MB
  87. 22. Hierarchical Clustering/9. HC in Python - Step 5.mp48.4MB
  88. 24. Apriori/1. Apriori Intuition.mp435.03MB
  89. 24. Apriori/2. How to get the dataset.mp411.71MB
  90. 24. Apriori/3. Apriori in R - Step 1.mp442.88MB
  91. 24. Apriori/4. Apriori in R - Step 2.mp430.5MB
  92. 24. Apriori/5. Apriori in R - Step 3.mp443.84MB
  93. 24. Apriori/6. Apriori in Python - Step 1.mp437.98MB
  94. 24. Apriori/7. Apriori in Python - Step 2.mp429.53MB
  95. 24. Apriori/8. Apriori in Python - Step 3.mp426.96MB
  96. 25. Eclat/1. Eclat Intuition.mp410.65MB
  97. 25. Eclat/2. How to get the dataset.mp411.71MB
  98. 25. Eclat/3. Eclat in R.mp420.68MB
  99. 27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp430.19MB
  100. 27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.mp447.21MB
  101. 27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.mp47.41MB
  102. 27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp429.33MB
  103. 27. Upper Confidence Bound (UCB)/3. How to get the dataset.mp411.72MB
  104. 27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp431.54MB
  105. 27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp435.45MB
  106. 27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp441.12MB
  107. 27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp49.13MB
  108. 27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.mp428.05MB
  109. 27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.mp429.01MB
  110. 28. Thompson Sampling/1. Thompson Sampling Intuition.mp437.28MB
  111. 28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp414.09MB
  112. 28. Thompson Sampling/3. How to get the dataset.mp411.72MB
  113. 28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp443.13MB
  114. 28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp48.41MB
  115. 28. Thompson Sampling/6. Thompson Sampling in R - Step 1.mp440.94MB
  116. 28. Thompson Sampling/7. Thompson Sampling in R - Step 2.mp47.47MB
  117. 29. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 7.mp417.11MB
  118. 29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.mp439.49MB
  119. 29. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 9.mp414.01MB
  120. 29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.mp424.13MB
  121. 29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp440.37MB
  122. 29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp417.47MB
  123. 29. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp413.52MB
  124. 29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp46.51MB
  125. 29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp44.57MB
  126. 29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.mp429.69MB
  127. 29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp412.73MB
  128. 29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp47.51MB
  129. 29. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp413.27MB
  130. 29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp428.99MB
  131. 29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp441.19MB
  132. 29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.mp411.71MB
  133. 29. -------------------- Part 7 Natural Language Processing --------------------/4. Natural Language Processing in Python - Step 1.mp435.21MB
  134. 29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.mp421.96MB
  135. 29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.mp43.39MB
  136. 29. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 4.mp424MB
  137. 29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.mp414.9MB
  138. 29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.mp46.49MB
  139. 30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp431.32MB
  140. 31. Artificial Neural Networks/1. Plan of attack.mp44.74MB
  141. 31. Artificial Neural Networks/10. Business Problem Description.mp416.37MB
  142. 31. Artificial Neural Networks/12. ANN in Python - Step 1.mp429.31MB
  143. 31. Artificial Neural Networks/13. ANN in Python - Step 2.mp448.1MB
  144. 31. Artificial Neural Networks/14. ANN in Python - Step 3.mp48.37MB
  145. 31. Artificial Neural Networks/15. ANN in Python - Step 4.mp45.88MB
  146. 31. Artificial Neural Networks/16. ANN in Python - Step 5.mp429.58MB
  147. 31. Artificial Neural Networks/17. ANN in Python - Step 6.mp47.06MB
  148. 31. Artificial Neural Networks/18. ANN in Python - Step 7.mp48.99MB
  149. 31. Artificial Neural Networks/19. ANN in Python - Step 8.mp418.16MB
  150. 31. Artificial Neural Networks/2. The Neuron.mp429.86MB
  151. 31. Artificial Neural Networks/20. ANN in Python - Step 9.mp416.89MB
  152. 31. Artificial Neural Networks/21. ANN in Python - Step 10.mp417.09MB
  153. 31. Artificial Neural Networks/22. ANN in R - Step 1.mp438.55MB
  154. 31. Artificial Neural Networks/23. ANN in R - Step 2.mp414.18MB
  155. 31. Artificial Neural Networks/24. ANN in R - Step 3.mp428.94MB
  156. 31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).mp433.44MB
  157. 31. Artificial Neural Networks/3. The Activation Function.mp414.76MB
  158. 31. Artificial Neural Networks/4. How do Neural Networks work.mp423.54MB
  159. 31. Artificial Neural Networks/5. How do Neural Networks learn.mp426.56MB
  160. 31. Artificial Neural Networks/6. Gradient Descent.mp418.54MB
  161. 31. Artificial Neural Networks/7. Stochastic Gradient Descent.mp416.83MB
  162. 31. Artificial Neural Networks/8. Backpropagation.mp410.93MB
  163. 31. Artificial Neural Networks/9. How to get the dataset.mp411.71MB
  164. 32. Convolutional Neural Networks/1. Plan of attack.mp45.9MB
  165. 32. Convolutional Neural Networks/10. How to get the dataset.mp411.72MB
  166. 32. Convolutional Neural Networks/12. CNN in Python - Step 1.mp424.93MB
  167. 32. Convolutional Neural Networks/13. CNN in Python - Step 2.mp45.85MB
  168. 32. Convolutional Neural Networks/14. CNN in Python - Step 3.mp42.22MB
  169. 32. Convolutional Neural Networks/15. CNN in Python - Step 4.mp427.18MB
  170. 32. Convolutional Neural Networks/16. CNN in Python - Step 5.mp49.91MB
  171. 32. Convolutional Neural Networks/17. CNN in Python - Step 6.mp49.71MB
  172. 32. Convolutional Neural Networks/18. CNN in Python - Step 7.mp412.93MB
  173. 32. Convolutional Neural Networks/19. CNN in Python - Step 8.mp46.79MB
  174. 32. Convolutional Neural Networks/2. What are convolutional neural networks.mp429.51MB
  175. 32. Convolutional Neural Networks/20. CNN in Python - Step 9.mp446.86MB
  176. 32. Convolutional Neural Networks/21. CNN in Python - Step 10.mp420.6MB
  177. 32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp431.03MB
  178. 32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp414.09MB
  179. 32. Convolutional Neural Networks/5. Step 2 - Pooling.mp440.24MB
  180. 32. Convolutional Neural Networks/6. Step 3 - Flattening.mp43.28MB
  181. 32. Convolutional Neural Networks/7. Step 4 - Full Connection.mp442.74MB
  182. 32. Convolutional Neural Networks/8. Summary.mp47.91MB
  183. 32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp433.23MB
  184. 34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp432.12MB
  185. 34. Principal Component Analysis (PCA)/2. How to get the dataset.mp411.72MB
  186. 34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp431.96MB
  187. 34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp422.08MB
  188. 34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.mp425.51MB
  189. 34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.mp430.66MB
  190. 34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.mp429.02MB
  191. 34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.mp436.74MB
  192. 35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp426.99MB
  193. 35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.mp411.71MB
  194. 35. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp445.42MB
  195. 35. Linear Discriminant Analysis (LDA)/4. LDA in R.mp451.29MB
  196. 36. Kernel PCA/1. How to get the dataset.mp411.72MB
  197. 36. Kernel PCA/2. Kernel PCA in Python.mp433.39MB
  198. 36. Kernel PCA/3. Kernel PCA in R.mp456.57MB
  199. 38. Model Selection/1. How to get the dataset.mp411.71MB
  200. 38. Model Selection/2. k-Fold Cross Validation in Python.mp432.83MB
  201. 38. Model Selection/3. k-Fold Cross Validation in R.mp443.63MB
  202. 38. Model Selection/4. Grid Search in Python - Step 1.mp438.22MB
  203. 38. Model Selection/5. Grid Search in Python - Step 2.mp429.51MB
  204. 38. Model Selection/6. Grid Search in R.mp435.55MB
  205. 39. XGBoost/1. How to get the dataset.mp411.72MB
  206. 39. XGBoost/2. XGBoost in Python - Step 1.mp421.39MB
  207. 39. XGBoost/3. XGBoost in Python - Step 2.mp431.97MB
  208. 39. XGBoost/4. XGBoost in R.mp447.27MB
  209. 4. Simple Linear Regression/1. How to get the dataset.mp411.72MB
  210. 4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp414.36MB
  211. 4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp48.64MB
  212. 4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp437.37MB
  213. 4. Simple Linear Regression/2. Dataset + Business Problem Description.mp46.63MB
  214. 4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.mp49.47MB
  215. 4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.mp45.37MB
  216. 4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.mp421.73MB
  217. 4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.mp418.75MB
  218. 4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.mp415.61MB
  219. 4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.mp430.82MB
  220. 4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp49.53MB
  221. 5. Multiple Linear Regression/1. How to get the dataset.mp411.71MB
  222. 5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp47.22MB
  223. 5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp414.3MB
  224. 5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.mp423.83MB
  225. 5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp432.59MB
  226. 5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp427.18MB
  227. 5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.mp417.94MB
  228. 5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.mp425.93MB
  229. 5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.mp410.42MB
  230. 5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp439.73MB
  231. 5. Multiple Linear Regression/2. Dataset + Business Problem Description.mp49.98MB
  232. 5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp417.25MB
  233. 5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.mp41.82MB
  234. 5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.mp41.78MB
  235. 5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.mp414.28MB
  236. 5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.mp44.52MB
  237. 5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.mp428.83MB
  238. 5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp439.57MB
  239. 6. Polynomial Regression/1. Polynomial Regression Intuition.mp49.44MB
  240. 6. Polynomial Regression/10. Polynomial Regression in R - Step 3.mp443.32MB
  241. 6. Polynomial Regression/11. Polynomial Regression in R - Step 4.mp422.34MB
  242. 6. Polynomial Regression/12. R Regression Template.mp425.42MB
  243. 6. Polynomial Regression/2. How to get the dataset.mp411.72MB
  244. 6. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp424.9MB
  245. 6. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp427.11MB
  246. 6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp442.98MB
  247. 6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp413.5MB
  248. 6. Polynomial Regression/7. Python Regression Template.mp427.43MB
  249. 6. Polynomial Regression/8. Polynomial Regression in R - Step 1.mp417.78MB
  250. 6. Polynomial Regression/9. Polynomial Regression in R - Step 2.mp423.88MB
  251. 7. Support Vector Regression (SVR)/1. How to get the dataset.mp411.72MB
  252. 7. Support Vector Regression (SVR)/2. SVR Intuition.mp446.59MB
  253. 7. Support Vector Regression (SVR)/3. SVR in Python.mp446.19MB
  254. 7. Support Vector Regression (SVR)/4. SVR in R.mp425.87MB
  255. 8. Decision Tree Regression/1. Decision Tree Regression Intuition.mp422.7MB
  256. 8. Decision Tree Regression/2. How to get the dataset.mp411.72MB
  257. 8. Decision Tree Regression/3. Decision Tree Regression in Python.mp433.55MB
  258. 8. Decision Tree Regression/4. Decision Tree Regression in R.mp444.38MB
  259. 9. Random Forest Regression/1. Random Forest Regression Intuition.mp413.82MB
  260. 9. Random Forest Regression/2. How to get the dataset.mp411.72MB
  261. 9. Random Forest Regression/3. Random Forest Regression in Python.mp439.48MB
  262. 9. Random Forest Regression/4. Random Forest Regression in R.mp440.35MB
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