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[FreeAllCourse.Com] Udemy- The Complete Machine Learning Course with Python

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 2019-12-24 17:45 2024-5-2 22:17 104 6.79 GB 108
磁力链接
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迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOmJlMWM5NTU5ZGRjOGVmYjEwNTY2NWE4ZDk3YWJjYTc3Yjk2MWQ4YzlaWg==
二维码链接
[FreeAllCourse.Com] Udemy- The Complete Machine Learning Course with Python的二维码
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相关链接

FreeAllCourse Com Udemy- The Complete Machine Learning Course with Python

文件列表
  1. 1. Introduction/1. What Does the Course Cover.mp454.4MB
  2. 10. Unsupervised Learning Clustering/1. Clustering.mp4125.68MB
  3. 10. Unsupervised Learning Clustering/2. k_Means Clustering.mp457.72MB
  4. 11. Deep Learning/1. Estimating Simple Function with Neural Networks.mp4143.85MB
  5. 11. Deep Learning/2. Neural Network Architecture.mp422.38MB
  6. 11. Deep Learning/3. Motivational Example - Project MNIST.mp4144.96MB
  7. 11. Deep Learning/4. Binary Classification Problem.mp472.11MB
  8. 11. Deep Learning/5. Natural Language Processing - Binary Classification.mp476.05MB
  9. 12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.mp413.75MB
  10. 12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.mp454.96MB
  11. 12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.mp418.67MB
  12. 12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.mp437.47MB
  13. 12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.mp470.06MB
  14. 12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.mp427.44MB
  15. 12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.mp420.85MB
  16. 12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.mp477.24MB
  17. 12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.mp4155.61MB
  18. 12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.mp440.61MB
  19. 12. Appendix A1 Foundations of Deep Learning/6. Why Now.mp49.07MB
  20. 12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.mp414.16MB
  21. 12. Appendix A1 Foundations of Deep Learning/8. Tensors.mp416.88MB
  22. 12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.mp488.79MB
  23. 13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.mp463.66MB
  24. 13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.mp4124.88MB
  25. 13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.mp4128.54MB
  26. 13. Computer Vision and Convolutional Neural Network (CNN)/12. Loading Previously Trained Model.mp411.21MB
  27. 13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.mp479.75MB
  28. 13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.mp428.48MB
  29. 13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.mp497MB
  30. 13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.mp4111.14MB
  31. 13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.mp435.41MB
  32. 13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.mp443.81MB
  33. 13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.mp466.21MB
  34. 13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.mp4141.94MB
  35. 13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.mp430.03MB
  36. 13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.mp429.13MB
  37. 13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.mp484.39MB
  38. 13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.mp432.32MB
  39. 13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.mp488.13MB
  40. 2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.mp438.42MB
  41. 2. Getting Started with Anaconda/2. Hello World.mp451.22MB
  42. 2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.mp489.84MB
  43. 2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.mp464.56MB
  44. 2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.mp455.87MB
  45. 2. Getting Started with Anaconda/6. Iris Project 4 Visualization.mp493.49MB
  46. 3. Regression/1. Scikit-Learn.mp448.45MB
  47. 3. Regression/10. Multiple Regression 2.mp491.15MB
  48. 3. Regression/11. Regularized Regression.mp444.35MB
  49. 3. Regression/12. Polynomial Regression.mp4110.78MB
  50. 3. Regression/13. Dealing with Non-linear Relationships.mp462.69MB
  51. 3. Regression/14. Feature Importance.mp436.25MB
  52. 3. Regression/15. Data Preprocessing.mp4135.55MB
  53. 3. Regression/16. Variance-Bias Trade Off.mp468.7MB
  54. 3. Regression/17. Learning Curve.mp456.37MB
  55. 3. Regression/18. Cross Validation.mp448.04MB
  56. 3. Regression/19. CV Illustration.mp4127.23MB
  57. 3. Regression/2. EDA.mp4151.67MB
  58. 3. Regression/3. Correlation Analysis and Feature Selection.mp422.58MB
  59. 3. Regression/4. Correlation Analysis and Feature Selection.mp4105.19MB
  60. 3. Regression/5. Linear Regression with Scikit-Learn.mp476.98MB
  61. 3. Regression/6. Five Steps Machine Learning Process.mp477.27MB
  62. 3. Regression/7. Robust Regression.mp4119.06MB
  63. 3. Regression/8. Evaluate Regression Model Performance.mp499.66MB
  64. 3. Regression/9. Multiple Regression 1.mp4125.51MB
  65. 4. Classification/1. Logistic Regression.mp4119.59MB
  66. 4. Classification/10. Precision Recall Tradeoff.mp4102.01MB
  67. 4. Classification/11. Altering the Precision Recall Tradeoff.mp420.93MB
  68. 4. Classification/12. ROC.mp452.22MB
  69. 4. Classification/2. Introduction to Classification.mp442.12MB
  70. 4. Classification/3. Understanding MNIST.mp4108.98MB
  71. 4. Classification/4. SGD.mp457.3MB
  72. 4. Classification/5. Performance Measure and Stratified k-Fold.mp451.54MB
  73. 4. Classification/6. Confusion Matrix.mp454.71MB
  74. 4. Classification/7. Precision.mp423.59MB
  75. 4. Classification/8. Recall.mp419.64MB
  76. 4. Classification/9. f1.mp412.11MB
  77. 5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.mp437.87MB
  78. 5. Support Vector Machine (SVM)/2. Linear SVM Classification.mp480.94MB
  79. 5. Support Vector Machine (SVM)/3. Polynomial Kernel.mp434.96MB
  80. 5. Support Vector Machine (SVM)/4. Radial Basis Function.mp470.13MB
  81. 5. Support Vector Machine (SVM)/5. Support Vector Regression.mp459.68MB
  82. 6. Tree/1. Introduction to Decision Tree.mp443.86MB
  83. 6. Tree/2. Training and Visualizing a Decision Tree.mp451.4MB
  84. 6. Tree/3. Visualizing Boundary.mp454.72MB
  85. 6. Tree/4. Tree Regression, Regularization and Over Fitting.mp440.05MB
  86. 6. Tree/5. End to End Modeling.mp435.62MB
  87. 6. Tree/6. Project HR.mp4177.83MB
  88. 6. Tree/7. Project HR with Google Colab.mp466.57MB
  89. 7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.mp437.17MB
  90. 7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.mp437.85MB
  91. 7. Ensemble Machine Learning/2. Bagging.mp4165.44MB
  92. 7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.mp480.28MB
  93. 7. Ensemble Machine Learning/4. AdaBoost.mp449.85MB
  94. 7. Ensemble Machine Learning/5. Gradient Boosting Machine.mp421.96MB
  95. 7. Ensemble Machine Learning/6. XGBoost Installation.mp422.26MB
  96. 7. Ensemble Machine Learning/7. XGBoost.mp435.05MB
  97. 7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.mp459.21MB
  98. 7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.mp446.4MB
  99. 8. k-Nearest Neighbours (kNN)/1. kNN Introduction.mp462.95MB
  100. 8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.mp475.73MB
  101. 8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.mp449.4MB
  102. 9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.mp431.37MB
  103. 9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.mp449.03MB
  104. 9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.mp447.87MB
  105. 9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.mp436.61MB
  106. 9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.mp421.44MB
  107. 9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.mp434.15MB
  108. 9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.mp430.74MB
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