[DesireCourse.Net] Udemy - The Complete Machine Learning Course with Python

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[DesireCourse.Net] Udemy - The Complete Machine Learning Course with Python.torrent

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种子哈希:B214CA1C763B2D13B1007E4C93226F42DC0B69B2
文件大小:6.79GB
创建时间:2020-03-27
下载热度:66395
下载速度:极快
最近访问:2020-03-27

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种子包含的文件

[DesireCourse.Net] Udemy - The Complete Machine Learning Course with Python.torrent

1. 6. Tree/6. Project HR.mp4 177.83MB

2. 7. Ensemble Machine Learning/2. Bagging.mp4 165.44MB

3. 12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.mp4 155.61MB

4. 3. Regression/2. EDA.mp4 151.67MB

5. 11. Deep Learning/3. Motivational Example - Project MNIST.mp4 144.96MB

6. 11. Deep Learning/1. Estimating Simple Function with Neural Networks.mp4 143.85MB

7. 13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.mp4 141.94MB

8. 3. Regression/15. Data Preprocessing.mp4 135.55MB

9. 13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.mp4 128.54MB

10. 3. Regression/19. CV Illustration.mp4 127.23MB

11. 10. Unsupervised Learning Clustering/1. Clustering.mp4 125.68MB

12. 3. Regression/9. Multiple Regression 1.mp4 125.51MB

13. 13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.mp4 124.88MB

14. 4. Classification/1. Logistic Regression.mp4 119.59MB

15. 3. Regression/7. Robust Regression.mp4 119.06MB

16. 13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.mp4 111.14MB

17. 3. Regression/12. Polynomial Regression.mp4 110.78MB

18. 4. Classification/3. Understanding MNIST.mp4 108.98MB

19. 3. Regression/4. Correlation Analysis and Feature Selection.mp4 105.19MB

20. 4. Classification/10. Precision Recall Tradeoff.mp4 102.01MB

21. 3. Regression/8. Evaluate Regression Model Performance.mp4 99.66MB

22. 13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.mp4 97.00MB

23. 2. Getting Started with Anaconda/6. Iris Project 4 Visualization.mp4 93.49MB

24. 3. Regression/10. Multiple Regression 2.mp4 91.15MB

25. 2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.mp4 89.84MB

26. 12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.mp4 88.79MB

27. 13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.mp4 88.13MB

28. 13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.mp4 84.39MB

29. 5. Support Vector Machine (SVM)/2. Linear SVM Classification.mp4 80.94MB

30. 7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.mp4 80.28MB

31. 13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.mp4 79.75MB

32. 3. Regression/6. Five Steps Machine Learning Process.mp4 77.27MB

33. 12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.mp4 77.24MB

34. 3. Regression/5. Linear Regression with Scikit-Learn.mp4 76.98MB

35. 11. Deep Learning/5. Natural Language Processing - Binary Classification.mp4 76.05MB

36. 8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.mp4 75.73MB

37. 11. Deep Learning/4. Binary Classification Problem.mp4 72.11MB

38. 5. Support Vector Machine (SVM)/4. Radial Basis Function.mp4 70.13MB

39. 12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.mp4 70.06MB

40. 3. Regression/16. Variance-Bias Trade Off.mp4 68.70MB

41. 6. Tree/7. Project HR with Google Colab.mp4 66.57MB

42. 13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.mp4 66.21MB

43. 2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.mp4 64.56MB

44. 13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.mp4 63.65MB

45. 8. k-Nearest Neighbours (kNN)/1. kNN Introduction.mp4 62.95MB

46. 3. Regression/13. Dealing with Non-linear Relationships.mp4 62.69MB

47. 5. Support Vector Machine (SVM)/5. Support Vector Regression.mp4 59.68MB

48. 7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.mp4 59.21MB

49. 10. Unsupervised Learning Clustering/2. k_Means Clustering.mp4 57.71MB

50. 4. Classification/4. SGD.mp4 57.30MB

51. 3. Regression/17. Learning Curve.mp4 56.37MB

52. 2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.mp4 55.87MB

53. 12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.mp4 54.96MB

54. 6. Tree/3. Visualizing Boundary.mp4 54.72MB

55. 4. Classification/6. Confusion Matrix.mp4 54.71MB

56. 1. Introduction/1. What Does the Course Cover.mp4 54.40MB

57. 4. Classification/12. ROC.mp4 52.22MB

58. 4. Classification/5. Performance Measure and Stratified k-Fold.mp4 51.54MB

59. 6. Tree/2. Training and Visualizing a Decision Tree.mp4 51.40MB

60. 2. Getting Started with Anaconda/2. Hello World.mp4 51.22MB

61. 7. Ensemble Machine Learning/4. AdaBoost.mp4 49.85MB

62. 8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.mp4 49.40MB

63. 9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.mp4 49.03MB

64. 3. Regression/1. Scikit-Learn.mp4 48.45MB

65. 3. Regression/18. Cross Validation.mp4 48.04MB

66. 9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.mp4 47.87MB

67. 7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.mp4 46.40MB

68. 3. Regression/11. Regularized Regression.mp4 44.35MB

69. 6. Tree/1. Introduction to Decision Tree.mp4 43.86MB

70. 13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.mp4 43.81MB

71. 4. Classification/2. Introduction to Classification.mp4 42.12MB

72. 12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.mp4 40.61MB

73. 6. Tree/4. Tree Regression, Regularization and Over Fitting.mp4 40.05MB

74. 2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.mp4 38.42MB

75. 5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.mp4 37.87MB

76. 7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.mp4 37.85MB

77. 12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.mp4 37.47MB

78. 7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.mp4 37.17MB

79. 9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.mp4 36.60MB

80. 3. Regression/14. Feature Importance.mp4 36.25MB

81. 6. Tree/5. End to End Modeling.mp4 35.62MB

82. 13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.mp4 35.41MB

83. 7. Ensemble Machine Learning/7. XGBoost.mp4 35.05MB

84. 5. Support Vector Machine (SVM)/3. Polynomial Kernel.mp4 34.96MB

85. 9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.mp4 34.15MB

86. 13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.mp4 32.32MB

87. 9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.mp4 31.37MB

88. 9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.mp4 30.74MB

89. 13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.mp4 30.03MB

90. 13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.mp4 29.13MB

91. 13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.mp4 28.48MB

92. 12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.mp4 27.44MB

93. 4. Classification/7. Precision.mp4 23.58MB

94. 3. Regression/3. Correlation Analysis and Feature Selection.mp4 22.58MB

95. 11. Deep Learning/2. Neural Network Architecture.mp4 22.37MB

96. 7. Ensemble Machine Learning/6. XGBoost Installation.mp4 22.26MB

97. 7. Ensemble Machine Learning/5. Gradient Boosting Machine.mp4 21.96MB

98. 9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.mp4 21.44MB

99. 4. Classification/11. Altering the Precision Recall Tradeoff.mp4 20.93MB

100. 12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.mp4 20.85MB

101. 4. Classification/8. Recall.mp4 19.64MB

102. 12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.mp4 18.67MB

103. 12. Appendix A1 Foundations of Deep Learning/8. Tensors.mp4 16.88MB

104. 12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.mp4 14.16MB

105. 12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.mp4 13.74MB

106. 4. Classification/9. f1.mp4 12.11MB

107. 13. Computer Vision and Convolutional Neural Network (CNN)/12. Loading Previously Trained Model.mp4 11.20MB

108. 12. Appendix A1 Foundations of Deep Learning/6. Why Now.mp4 9.06MB

109. 3. Regression/3.1 0305.zip 2.13MB

110. 8. k-Nearest Neighbours (kNN)/4.1 0805.zip 40.76KB

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