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[] Udemy - machine-learning-course-with-python

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  • 文件大小:3GB
  • 下载次数:413
  • 最近下载:2021-01-07 19:22:53
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文件列表

  1. 07 Ensemble Machine Learning/068 Project HR - Human Resources Analytics.mp4 88MB
  2. 02 Getting Started with Anaconda/004 Windows OS Downloading Installing Anaconda.mp4 64MB
  3. 03 Regression/015 Boston Housing Data - EDA.mp4 61MB
  4. 03 Regression/020 Multiple Regression with statsmodel.mp4 60MB
  5. 03 Regression/019 Evaluate Model Performance.mp4 59MB
  6. 06 Tree/057 Project HR - Loading and preprocesing data.mp4 57MB
  7. 10 Unsupervised Learning Clustering/091 Truncating Dendrogram.mp4 56MB
  8. 07 Ensemble Machine Learning/061 Bagging Part 1.mp4 55MB
  9. 06 Tree/052 Visualising a Decision Trees.mp4 55MB
  10. 06 Tree/055 Overfitting and Grid Search.mp4 54MB
  11. 07 Ensemble Machine Learning/069 Ensemble of ensembles Part 1.mp4 52MB
  12. 05 Support Vector Machine SVM/043 Support Vector Machine SVM Concepts.mp4 52MB
  13. 07 Ensemble Machine Learning/067 XGBoost.mp4 51MB
  14. 05 Support Vector Machine SVM/045 Polynomial Kernel.mp4 50MB
  15. 03 Regression/022 Ordinary Least Square Regression and Gradient Descent.mp4 50MB
  16. 03 Regression/028 Data Pre-Processing 2.mp4 50MB
  17. 08 k-Nearest Neighbours kNN/075 Project Cancer Detection Part 1.mp4 49MB
  18. 08 k-Nearest Neighbours kNN/076 Project Cancer Detection Part 2.mp4 49MB
  19. 03 Regression/023 Regularised Method for Regression.mp4 48MB
  20. 02 Getting Started with Anaconda/011 Presenting Your Data.mp4 47MB
  21. 09 Dimensionality Reduction/081 Project Wine 1 Dimensionality Reduction with PCA.mp4 46MB
  22. 04 Classification/038 MNIST Project 4 - Confusion Matrix Precision Recall and F1 Score.mp4 46MB
  23. 03 Regression/014 Working with Scikit-Learn.mp4 46MB
  24. 07 Ensemble Machine Learning/070 Ensemble of ensembles Part 2.mp4 45MB
  25. 07 Ensemble Machine Learning/066 Gradient Boosting Machine.mp4 45MB
  26. 05 Support Vector Machine SVM/046 Gaussian Radial Basis Function.mp4 45MB
  27. 03 Regression/031 Cross Validation.mp4 45MB
  28. 03 Regression/021 Multiple Regression and Feature Importance.mp4 45MB
  29. 10 Unsupervised Learning Clustering/090 Wards Agglomerative Hierarchical Clustering.mp4 44MB
  30. 04 Classification/039 MNIST Project 5 - Precision and Recall Tradeoff.mp4 44MB
  31. 03 Regression/029 Variance Bias Trade Off - Validation Curve.mp4 44MB
  32. 03 Regression/024 Polynomial Regression.mp4 44MB
  33. 07 Ensemble Machine Learning/063 Random Forests.mp4 43MB
  34. 03 Regression/030 Variance Bias Trade Off - Learning Curve.mp4 42MB
  35. 09 Dimensionality Reduction/079 PCA Introduction.mp4 42MB
  36. 04 Classification/034 Logistic Regression 2.mp4 42MB
  37. 02 Getting Started with Anaconda/008 Navigating the Spyder Jupyter Notebook Interface.mp4 41MB
  38. 07 Ensemble Machine Learning/065 AdaBoost.mp4 40MB
  39. 07 Ensemble Machine Learning/062 Bagging Part 2.mp4 37MB
  40. 06 Tree/053 Decision Tree Learning Algorithm.mp4 37MB
  41. 10 Unsupervised Learning Clustering/092 k-Means Clustering.mp4 37MB
  42. 09 Dimensionality Reduction/084 Kernel PCA.mp4 36MB
  43. 03 Regression/027 Data Pre-Processing 1.mp4 35MB
  44. 02 Getting Started with Anaconda/010 Data Exploration and Analysis.mp4 35MB
  45. 04 Classification/035 MNIST Project 1 - Introduction.mp4 35MB
  46. 06 Tree/050 What is Decision Tree.mp4 34MB
  47. 06 Tree/054 Decision Tree Regression.mp4 34MB
  48. 03 Regression/017 Simple Linear Regression Modelling with Boston Housing Data.mp4 34MB
  49. 04 Classification/040 MNIST Project 6 - The ROC Curve.mp4 34MB
  50. 03 Regression/018 Robust Regression.mp4 33MB
  51. 05 Support Vector Machine SVM/044 Linear SVM Classification.mp4 32MB
  52. 03 Regression/025 Dealing with Non-linear relationships.mp4 28MB
  53. 07 Ensemble Machine Learning/060 Ensemble Learning Methods Introduction.mp4 28MB
  54. 04 Classification/033 Logistic Regression 1.mp4 27MB
  55. 04 Classification/037 MNIST Project 3 - Performance Measures.mp4 26MB
  56. 03 Regression/026 Feature Importance Revisited.mp4 26MB
  57. 09 Dimensionality Reduction/078 Dimensionality Reduction Concept.mp4 26MB
  58. 10 Unsupervised Learning Clustering/095 Mean Shift.mp4 26MB
  59. 04 Classification/036 MNIST Project 2 - SGDClassifier.mp4 25MB
  60. 10 Unsupervised Learning Clustering/089 MLextend.mp4 23MB
  61. 03 Regression/016 Correlation Analysis and Feature Selection.mp4 23MB
  62. 03 Regression/013 Categories of Machine Learning.mp4 22MB
  63. 07 Ensemble Machine Learning/064 Extra-Trees.mp4 22MB
  64. 08 k-Nearest Neighbours kNN/073 kNN and Iris Dataset Demo.mp4 21MB
  65. 02 Getting Started with Anaconda/005 Windows OS Managing Environment.mp4 19MB
  66. 09 Dimensionality Reduction/083 Project Wine 2 Choosing the Number of Components.mp4 19MB
  67. 10 Unsupervised Learning Clustering/088 Clustering Concepts.mp4 17MB
  68. 05 Support Vector Machine SVM/047 Support Vector Regression.mp4 17MB
  69. 09 Dimensionality Reduction/086 LDA Comparison between LDA and PCA.mp4 17MB
  70. 06 Tree/051 Training a Decision Tree.mp4 17MB
  71. 06 Tree/058 Project HR - Modelling.mp4 17MB
  72. 10 Unsupervised Learning Clustering/094 Silhouette Analysis.mp4 16MB
  73. 09 Dimensionality Reduction/085 Kernel PCA Demo.mp4 16MB
  74. 10 Unsupervised Learning Clustering/093 Elbow Method.mp4 16MB
  75. 08 k-Nearest Neighbours kNN/072 kNN Concepts.mp4 15MB
  76. 09 Dimensionality Reduction/080 Dimensionality Reduction Demo.mp4 15MB
  77. 05 Support Vector Machine SVM/048 Advantages and Disadvantages of SVM.mp4 13MB
  78. 08 k-Nearest Neighbours kNN/074 Distance Metric.mp4 13MB
  79. 03 Regression/012 Introduction.mp4 12MB
  80. 06 Tree/056 Where to From Here.mp4 12MB
  81. 02 Getting Started with Anaconda/009 Downloading the IRIS Datasets.mp4 10MB
  82. 01 Introduction/001 What Does the Course Cover.mp4 10MB
  83. 04 Classification/032 Introduction.mp4 8MB
  84. 06 Tree/049 Introduction.mp4 6MB
  85. 07 Ensemble Machine Learning/059 Introduction.mp4 5MB
  86. 05 Support Vector Machine SVM/042 Introduction.mp4 5MB
  87. 08 k-Nearest Neighbours kNN/071 kNN Introduction.mp4 4MB
  88. 10 Unsupervised Learning Clustering/087 Introduction.mp4 4MB
  89. 09 Dimensionality Reduction/077 Introduction.mp4 4MB
  90. 03 Regression/attached_files/019 Evaluate Model Performance/0308.zip 3MB
  91. 03 Regression/attached_files/018 Robust Regression/0307.zip 3MB
  92. 03 Regression/attached_files/021 Multiple Regression and Feature Importance/0310.zip 3MB
  93. 03 Regression/attached_files/020 Multiple Regression with statsmodel/0309.zip 3MB
  94. 03 Regression/attached_files/017 Simple Linear Regression Modelling with Boston Housing Data/0306.zip 3MB
  95. 03 Regression/attached_files/016 Correlation Analysis and Feature Selection/0305.zip 2MB
  96. 03 Regression/attached_files/015 Boston Housing Data - EDA/0304.zip 2MB
  97. 02 Getting Started with Anaconda/attached_files/011 Presenting Your Data/0207.zip 1MB
  98. 03 Regression/attached_files/014 Working with Scikit-Learn/0303.zip 912KB
  99. 03 Regression/attached_files/030 Variance Bias Trade Off - Learning Curve/0319.zip 716KB
  100. 04 Classification/attached_files/040 MNIST Project 6 - The ROC Curve/0410.zip 684KB
  101. 04 Classification/attached_files/039 MNIST Project 5 - Precision and Recall Tradeoff/0409.zip 630KB
  102. 04 Classification/attached_files/038 MNIST Project 4 - Confusion Matrix Precision Recall and F1 Score/0408.zip 574KB
  103. 05 Support Vector Machine SVM/attached_files/047 Support Vector Regression/0506.zip 573KB
  104. 04 Classification/attached_files/037 MNIST Project 3 - Performance Measures/0407.zip 566KB
  105. 04 Classification/attached_files/035 MNIST Project 1 - Introduction/0405.zip 566KB
  106. 04 Classification/attached_files/036 MNIST Project 2 - SGDClassifier/0406.zip 566KB
  107. 05 Support Vector Machine SVM/attached_files/046 Gaussian Radial Basis Function/0505.zip 533KB
  108. 03 Regression/attached_files/029 Variance Bias Trade Off - Validation Curve/0318.zip 528KB
  109. 05 Support Vector Machine SVM/attached_files/045 Polynomial Kernel/0504.zip 491KB
  110. 05 Support Vector Machine SVM/attached_files/044 Linear SVM Classification/0503.zip 447KB
  111. 10 Unsupervised Learning Clustering/attached_files/088 Clustering Concepts/1002.zip 440KB
  112. 10 Unsupervised Learning Clustering/attached_files/089 MLextend/1003.zip 440KB
  113. 10 Unsupervised Learning Clustering/attached_files/090 Wards Agglomerative Hierarchical Clustering/1004.zip 440KB
  114. 10 Unsupervised Learning Clustering/attached_files/091 Truncating Dendrogram/1005.zip 440KB
  115. 10 Unsupervised Learning Clustering/attached_files/092 k-Means Clustering/1006.zip 440KB
  116. 10 Unsupervised Learning Clustering/attached_files/093 Elbow Method/1007.zip 440KB
  117. 10 Unsupervised Learning Clustering/attached_files/094 Silhouette Analysis/1008.zip 440KB
  118. 10 Unsupervised Learning Clustering/attached_files/095 Mean Shift/1009.zip 440KB
  119. 03 Regression/attached_files/024 Polynomial Regression/0313.zip 375KB
  120. 06 Tree/attached_files/056 Where to From Here/0608.zip 310KB
  121. 06 Tree/attached_files/055 Overfitting and Grid Search/0607.zip 298KB
  122. 06 Tree/attached_files/054 Decision Tree Regression/0606.zip 278KB
  123. 03 Regression/attached_files/023 Regularised Method for Regression/0312.zip 259KB
  124. 06 Tree/attached_files/053 Decision Tree Learning Algorithm/0605.zip 229KB
  125. 02 Getting Started with Anaconda/attached_files/009 Downloading the IRIS Datasets/0205.zip 229KB
  126. 03 Regression/attached_files/026 Feature Importance Revisited/0315.zip 226KB
  127. 05 Support Vector Machine SVM/attached_files/043 Support Vector Machine SVM Concepts/0502.zip 219KB
  128. 03 Regression/attached_files/025 Dealing with Non-linear relationships/0314.zip 181KB
  129. 06 Tree/attached_files/052 Visualising a Decision Trees/0604.zip 159KB
  130. 03 Regression/attached_files/028 Data Pre-Processing 2/0317.zip 157KB
  131. 03 Regression/attached_files/027 Data Pre-Processing 1/0316.zip 156KB
  132. 03 Regression/attached_files/022 Ordinary Least Square Regression and Gradient Descent/0311.zip 141KB
  133. 07 Ensemble Machine Learning/attached_files/068 Project HR - Human Resources Analytics/0710.zip 127KB
  134. 06 Tree/attached_files/051 Training a Decision Tree/0603.zip 114KB
  135. 06 Tree/attached_files/050 What is Decision Tree/0602.zip 113KB
  136. 09 Dimensionality Reduction/attached_files/084 Kernel PCA/0907.zip 108KB
  137. 09 Dimensionality Reduction/attached_files/085 Kernel PCA Demo/0908.zip 108KB
  138. 09 Dimensionality Reduction/attached_files/083 Project Wine 2 Choosing the Number of Components/0906.zip 106KB
  139. 03 Regression/attached_files/031 Cross Validation/0320.zip 95KB
  140. 09 Dimensionality Reduction/attached_files/079 PCA Introduction/0903.zip 87KB
  141. 09 Dimensionality Reduction/attached_files/086 LDA Comparison between LDA and PCA/0909.zip 77KB
  142. 06 Tree/attached_files/058 Project HR - Modelling/0610.zip 64KB
  143. 06 Tree/attached_files/057 Project HR - Loading and preprocesing data/0609.zip 63KB
  144. 07 Ensemble Machine Learning/attached_files/066 Gradient Boosting Machine/0708.zip 60KB
  145. 02 Getting Started with Anaconda/attached_files/010 Data Exploration and Analysis/0206.zip 45KB
  146. 02 Getting Started with Anaconda/attached_files/008 Navigating the Spyder Jupyter Notebook Interface/0204.zip 42KB
  147. 08 k-Nearest Neighbours kNN/attached_files/075 Project Cancer Detection Part 1/0805.zip 41KB
  148. 08 k-Nearest Neighbours kNN/attached_files/076 Project Cancer Detection Part 2/0806.zip 41KB
  149. 08 k-Nearest Neighbours kNN/attached_files/073 kNN and Iris Dataset Demo/0803.zip 26KB
  150. 04 Classification/attached_files/034 Logistic Regression 2/0404.zip 20KB
  151. 04 Classification/attached_files/033 Logistic Regression 1/0403.zip 19KB
  152. 09 Dimensionality Reduction/attached_files/082 Project Abalone/09-Project-Abalone.zip 16KB
  153. 03 Regression/quizzes/003 Section 3.html 11KB
  154. 02 Getting Started with Anaconda/quizzes/001 Getting Started.html 11KB
  155. 07 Ensemble Machine Learning/attached_files/064 Extra-Trees/0706.zip 9KB
  156. 03 Regression/quizzes/002 Machine Learning Basic Concepts.html 9KB
  157. 07 Ensemble Machine Learning/attached_files/065 AdaBoost/0707.zip 9KB
  158. 07 Ensemble Machine Learning/attached_files/063 Random Forests/0705.zip 9KB
  159. 07 Ensemble Machine Learning/attached_files/062 Bagging Part 2/0704.zip 8KB
  160. 07 Ensemble Machine Learning/attached_files/061 Bagging Part 1/0703.zip 8KB
  161. 07 Ensemble Machine Learning/attached_files/070 Ensemble of ensembles Part 2/0712.zip 5KB
  162. 07 Ensemble Machine Learning/attached_files/069 Ensemble of ensembles Part 1/0711.zip 4KB
  163. 07 Ensemble Machine Learning/attached_files/067 XGBoost/0709.zip 3KB
  164. 07 Ensemble Machine Learning/attached_files/060 Ensemble Learning Methods Introduction/0702.zip 2KB
  165. 09 Dimensionality Reduction/attached_files/078 Dimensionality Reduction Concept/0902.zip 2KB
  166. 01 Introduction/002 How to Succeed in This Course.html 2KB
  167. 01 Introduction/003 Project Files.html 2KB
  168. 08 k-Nearest Neighbours kNN/attached_files/072 kNN Concepts/0802.zip 2KB
  169. 04 Classification/041 MNIST Exercise.html 826B
  170. 02 Getting Started with Anaconda/007 Practice Activity Create a New Environment.html 737B
  171. 02 Getting Started with Anaconda/006 Mac OS Intructions on Installing Anaconda and Managing Environment.html 516B
  172. 09 Dimensionality Reduction/082 Project Abalone.html 458B
  173. [Discuss.FreeTutorials.Us].url 252B
  174. [FreeCoursesOnline.Us].url 123B
  175. [FreeTutorials.Us].url 119B
  176. Use VLC Player for Subtitles.url 115B