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[] Udemy - The Complete Machine Learning Course with Python

  • 收录时间:2020-10-31 12:15:57
  • 文件大小:7GB
  • 下载次数:6
  • 最近下载:2021-01-17 17:24:18
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文件列表

  1. 6. Tree/6. Project HR.mp4 178MB
  2. 7. Ensemble Machine Learning/2. Bagging.mp4 165MB
  3. 12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.mp4 156MB
  4. 3. Regression/2. EDA.mp4 152MB
  5. 11. Deep Learning/3. Motivational Example - Project MNIST.mp4 145MB
  6. 11. Deep Learning/1. Estimating Simple Function with Neural Networks.mp4 144MB
  7. 13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.mp4 142MB
  8. 3. Regression/15. Data Preprocessing.mp4 136MB
  9. 13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.mp4 129MB
  10. 3. Regression/19. CV Illustration.mp4 127MB
  11. 10. Unsupervised Learning Clustering/1. Clustering.mp4 126MB
  12. 3. Regression/9. Multiple Regression 1.mp4 126MB
  13. 13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.mp4 125MB
  14. 4. Classification/1. Logistic Regression.mp4 120MB
  15. 3. Regression/7. Robust Regression.mp4 119MB
  16. 13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.mp4 111MB
  17. 3. Regression/12. Polynomial Regression.mp4 111MB
  18. 4. Classification/3. Understanding MNIST.mp4 109MB
  19. 3. Regression/4. Correlation Analysis and Feature Selection.mp4 105MB
  20. 4. Classification/10. Precision Recall Tradeoff.mp4 102MB
  21. 3. Regression/8. Evaluate Regression Model Performance.mp4 100MB
  22. 13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.mp4 97MB
  23. 2. Getting Started with Anaconda/6. Iris Project 4 Visualization.mp4 93MB
  24. 3. Regression/10. Multiple Regression 2.mp4 91MB
  25. 2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.mp4 90MB
  26. 12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.mp4 89MB
  27. 13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.mp4 88MB
  28. 13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.mp4 84MB
  29. 5. Support Vector Machine (SVM)/2. Linear SVM Classification.mp4 81MB
  30. 7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.mp4 80MB
  31. 13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.mp4 80MB
  32. 3. Regression/6. Five Steps Machine Learning Process.mp4 77MB
  33. 12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.mp4 77MB
  34. 3. Regression/5. Linear Regression with Scikit-Learn.mp4 77MB
  35. 11. Deep Learning/5. Natural Language Processing - Binary Classification.mp4 76MB
  36. 8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.mp4 76MB
  37. 11. Deep Learning/4. Binary Classification Problem.mp4 72MB
  38. 5. Support Vector Machine (SVM)/4. Radial Basis Function.mp4 70MB
  39. 12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.mp4 70MB
  40. 3. Regression/16. Variance-Bias Trade Off.mp4 69MB
  41. 6. Tree/7. Project HR with Google Colab.mp4 67MB
  42. 13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.mp4 66MB
  43. 2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.mp4 65MB
  44. 13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.mp4 64MB
  45. 8. k-Nearest Neighbours (kNN)/1. kNN Introduction.mp4 63MB
  46. 3. Regression/13. Dealing with Non-linear Relationships.mp4 63MB
  47. 5. Support Vector Machine (SVM)/5. Support Vector Regression.mp4 60MB
  48. 7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.mp4 59MB
  49. 10. Unsupervised Learning Clustering/2. k_Means Clustering.mp4 58MB
  50. 4. Classification/4. SGD.mp4 57MB
  51. 3. Regression/17. Learning Curve.mp4 56MB
  52. 2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.mp4 56MB
  53. 12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.mp4 55MB
  54. 6. Tree/3. Visualizing Boundary.mp4 55MB
  55. 4. Classification/6. Confusion Matrix.mp4 55MB
  56. 1. Introduction/1. What Does the Course Cover.mp4 54MB
  57. 4. Classification/12. ROC.mp4 52MB
  58. 4. Classification/5. Performance Measure and Stratified k-Fold.mp4 52MB
  59. 6. Tree/2. Training and Visualizing a Decision Tree.mp4 51MB
  60. 2. Getting Started with Anaconda/2. Hello World.mp4 51MB
  61. 7. Ensemble Machine Learning/4. AdaBoost.mp4 50MB
  62. 8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.mp4 49MB
  63. 9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.mp4 49MB
  64. 3. Regression/1. Scikit-Learn.mp4 48MB
  65. 3. Regression/18. Cross Validation.mp4 48MB
  66. 9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.mp4 48MB
  67. 7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.mp4 46MB
  68. 3. Regression/11. Regularized Regression.mp4 44MB
  69. 6. Tree/1. Introduction to Decision Tree.mp4 44MB
  70. 13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.mp4 44MB
  71. 4. Classification/2. Introduction to Classification.mp4 42MB
  72. 12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.mp4 41MB
  73. 6. Tree/4. Tree Regression, Regularization and Over Fitting.mp4 40MB
  74. 2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.mp4 38MB
  75. 5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.mp4 38MB
  76. 7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.mp4 38MB
  77. 12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.mp4 37MB
  78. 7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.mp4 37MB
  79. 9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.mp4 37MB
  80. 3. Regression/14. Feature Importance.mp4 36MB
  81. 6. Tree/5. End to End Modeling.mp4 36MB
  82. 13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.mp4 35MB
  83. 7. Ensemble Machine Learning/7. XGBoost.mp4 35MB
  84. 5. Support Vector Machine (SVM)/3. Polynomial Kernel.mp4 35MB
  85. 9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.mp4 34MB
  86. 13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.mp4 32MB
  87. 9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.mp4 31MB
  88. 9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.mp4 31MB
  89. 13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.mp4 30MB
  90. 13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.mp4 29MB
  91. 13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.mp4 28MB
  92. 12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.mp4 27MB
  93. 4. Classification/7. Precision.mp4 24MB
  94. 3. Regression/3. Correlation Analysis and Feature Selection.mp4 23MB
  95. 11. Deep Learning/2. Neural Network Architecture.mp4 22MB
  96. 7. Ensemble Machine Learning/6. XGBoost Installation.mp4 22MB
  97. 7. Ensemble Machine Learning/5. Gradient Boosting Machine.mp4 22MB
  98. 9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.mp4 21MB
  99. 4. Classification/11. Altering the Precision Recall Tradeoff.mp4 21MB
  100. 12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.mp4 21MB
  101. 4. Classification/8. Recall.mp4 20MB
  102. 12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.mp4 19MB
  103. 12. Appendix A1 Foundations of Deep Learning/8. Tensors.mp4 17MB
  104. 12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.mp4 14MB
  105. 12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.mp4 14MB
  106. 4. Classification/9. f1.mp4 12MB
  107. 13. Computer Vision and Convolutional Neural Network (CNN)/12. Loading Previously Trained Model.mp4 11MB
  108. 12. Appendix A1 Foundations of Deep Learning/6. Why Now.mp4 9MB
  109. 3. Regression/3.1 0305.zip 2MB
  110. 8. k-Nearest Neighbours (kNN)/4.1 0805.zip 41KB
  111. 6. Tree/6. Project HR.srt 31KB
  112. 3. Regression/15. Data Preprocessing.srt 28KB
  113. 11. Deep Learning/1. Estimating Simple Function with Neural Networks.srt 26KB
  114. 12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.srt 26KB
  115. 11. Deep Learning/3. Motivational Example - Project MNIST.srt 26KB
  116. 4. Classification/1. Logistic Regression.srt 25KB
  117. 8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.srt 25KB
  118. 3. Regression/2. EDA.srt 24KB
  119. 3. Regression/9. Multiple Regression 1.srt 24KB
  120. 13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.srt 24KB
  121. 7. Ensemble Machine Learning/2. Bagging.srt 23KB
  122. 4. Classification/10. Precision Recall Tradeoff.srt 22KB
  123. 3. Regression/12. Polynomial Regression.srt 22KB
  124. 3. Regression/7. Robust Regression.srt 22KB
  125. 3. Regression/19. CV Illustration.srt 21KB
  126. 12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.srt 21KB
  127. 13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.srt 21KB
  128. 10. Unsupervised Learning Clustering/1. Clustering.srt 21KB
  129. 3. Regression/8. Evaluate Regression Model Performance.srt 19KB
  130. 4. Classification/3. Understanding MNIST.srt 18KB
  131. 12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.srt 18KB
  132. 13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.srt 17KB
  133. 13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.srt 17KB
  134. 2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.srt 16KB
  135. 3. Regression/5. Linear Regression with Scikit-Learn.srt 16KB
  136. 3. Regression/10. Multiple Regression 2.srt 15KB
  137. 3. Regression/4. Correlation Analysis and Feature Selection.srt 15KB
  138. 3. Regression/16. Variance-Bias Trade Off.srt 15KB
  139. 2. Getting Started with Anaconda/2. Hello World.srt 14KB
  140. 13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.srt 14KB
  141. 13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.srt 14KB
  142. 12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.srt 14KB
  143. 5. Support Vector Machine (SVM)/2. Linear SVM Classification.srt 13KB
  144. 13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.srt 13KB
  145. 11. Deep Learning/5. Natural Language Processing - Binary Classification.srt 13KB
  146. 12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.srt 13KB
  147. 6. Tree/7. Project HR with Google Colab.srt 13KB
  148. 12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.srt 13KB
  149. 2. Getting Started with Anaconda/6. Iris Project 4 Visualization.srt 12KB
  150. 11. Deep Learning/4. Binary Classification Problem.srt 12KB
  151. 12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.srt 12KB
  152. 8. k-Nearest Neighbours (kNN)/1. kNN Introduction.srt 12KB
  153. 4. Classification/6. Confusion Matrix.srt 12KB
  154. 13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.srt 12KB
  155. 7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.srt 12KB
  156. 4. Classification/4. SGD.srt 11KB
  157. 3. Regression/13. Dealing with Non-linear Relationships.srt 11KB
  158. 3. Regression/1. Scikit-Learn.srt 11KB
  159. 3. Regression/17. Learning Curve.srt 11KB
  160. 10. Unsupervised Learning Clustering/2. k_Means Clustering.srt 11KB
  161. 2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.srt 11KB
  162. 2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.srt 11KB
  163. 3. Regression/3. Correlation Analysis and Feature Selection.srt 11KB
  164. 8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.srt 11KB
  165. 7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.srt 10KB
  166. 3. Regression/18. Cross Validation.srt 10KB
  167. 3. Regression/6. Five Steps Machine Learning Process.srt 10KB
  168. 13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.srt 10KB
  169. 5. Support Vector Machine (SVM)/5. Support Vector Regression.srt 10KB
  170. 6. Tree/3. Visualizing Boundary.srt 10KB
  171. 13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.srt 9KB
  172. 5. Support Vector Machine (SVM)/4. Radial Basis Function.srt 9KB
  173. 9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.srt 9KB
  174. 4. Classification/5. Performance Measure and Stratified k-Fold.srt 9KB
  175. 6. Tree/1. Introduction to Decision Tree.srt 9KB
  176. 5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.srt 9KB
  177. 3. Regression/11. Regularized Regression.srt 8KB
  178. 4. Classification/12. ROC.srt 8KB
  179. 7. Ensemble Machine Learning/4. AdaBoost.srt 8KB
  180. 11. Deep Learning/2. Neural Network Architecture.srt 8KB
  181. 13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.srt 8KB
  182. 7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.srt 8KB
  183. 9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.srt 8KB
  184. 6. Tree/2. Training and Visualizing a Decision Tree.srt 7KB
  185. 13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.srt 7KB
  186. 13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.srt 7KB
  187. 13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.srt 7KB
  188. 2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.srt 7KB
  189. 9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.srt 7KB
  190. 9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.srt 6KB
  191. 7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.srt 6KB
  192. 4. Classification/2. Introduction to Classification.srt 6KB
  193. 5. Support Vector Machine (SVM)/3. Polynomial Kernel.srt 6KB
  194. 7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.srt 6KB
  195. 12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.srt 6KB
  196. 12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.srt 6KB
  197. 3. Regression/14. Feature Importance.srt 6KB
  198. 9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.srt 6KB
  199. 12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.srt 6KB
  200. 6. Tree/4. Tree Regression, Regularization and Over Fitting.srt 6KB
  201. 6. Tree/5. End to End Modeling.srt 6KB
  202. 7. Ensemble Machine Learning/7. XGBoost.srt 5KB
  203. 12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.srt 5KB
  204. 9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.srt 5KB
  205. 12. Appendix A1 Foundations of Deep Learning/8. Tensors.srt 5KB
  206. 13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.srt 5KB
  207. 4. Classification/7. Precision.srt 4KB
  208. 4. Classification/8. Recall.srt 4KB
  209. 9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.srt 4KB
  210. 4. Classification/11. Altering the Precision Recall Tradeoff.srt 4KB
  211. 7. Ensemble Machine Learning/5. Gradient Boosting Machine.srt 4KB
  212. 13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.srt 4KB
  213. 12. Appendix A1 Foundations of Deep Learning/6. Why Now.srt 3KB
  214. 1. Introduction/1. What Does the Course Cover.srt 3KB
  215. 7. Ensemble Machine Learning/6. XGBoost Installation.srt 3KB
  216. 12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.srt 3KB
  217. 4. Classification/9. f1.srt 2KB
  218. 1. Introduction/2. How to Succeed in This Course.html 2KB
  219. 1. Introduction/3. Project Files and Resources.html 2KB
  220. 13. Computer Vision and Convolutional Neural Network (CNN)/12. Loading Previously Trained Model.srt 2KB
  221. 8. k-Nearest Neighbours (kNN)/3. Addition Materials.html 335B
  222. [FreeCourseWorld.Com].url 54B