• Урок 1. 00:02:33
    What Does the Course Cover?
  • Урок 2. 00:05:18
    Installing Applications and Creating Environment
  • Урок 3. 00:10:58
    Hello World
  • Урок 4. 00:12:33
    Iris Project 1: Working with Error Messages
  • Урок 5. 00:08:46
    Iris Project 2: Reading CSV Data into Memory
  • Урок 6. 00:08:44
    Iris Project 3: Loading data from Seaborn
  • Урок 7. 00:10:21
    Iris Project 4: Visualization
  • Урок 8. 00:09:12
    Scikit-Learn
  • Урок 9. 00:19:12
    EDA
  • Урок 10. 00:08:48
    Correlation Analysis and Feature Selection
  • Урок 11. 00:13:04
    Correlation Analysis and Feature Selection
  • Урок 12. 00:13:45
    Linear Regression with Scikit-Learn
  • Урок 13. 00:08:54
    Five Steps Machine Learning Process
  • Урок 14. 00:18:01
    Robust Regression
  • Урок 15. 00:15:40
    Evaluate Regression Model Performance
  • Урок 16. 00:19:45
    Multiple Regression 1
  • Урок 17. 00:12:28
    Multiple Regression 2
  • Урок 18. 00:06:54
    Regularized Regression
  • Урок 19. 00:18:04
    Polynomial Regression
  • Урок 20. 00:09:32
    Dealing with Non-linear Relationships
  • Урок 21. 00:05:14
    Feature Importance
  • Урок 22. 00:22:00
    Data Preprocessing
  • Урок 23. 00:11:44
    Variance-Bias Trade Off
  • Урок 24. 00:08:39
    Learning Curve
  • Урок 25. 00:08:03
    Cross Validation
  • Урок 26. 00:17:45
    CV Illustration
  • Урок 27. 00:20:53
    Logistic Regression
  • Урок 28. 00:05:05
    Introduction to Classification
  • Урок 29. 00:14:57
    Understanding MNIST
  • Урок 30. 00:09:30
    SGD
  • Урок 31. 00:07:27
    Performance Measure and Stratified k-Fold
  • Урок 32. 00:09:23
    Confusion Matrix
  • Урок 33. 00:03:39
    Precision
  • Урок 34. 00:03:19
    Recall
  • Урок 35. 00:02:05
    f1
  • Урок 36. 00:18:03
    Precision Recall Tradeoff
  • Урок 37. 00:03:08
    Altering the Precision Recall Tradeoff
  • Урок 38. 00:07:01
    ROC
  • Урок 39. 00:06:58
    Support Vector Machine (SVM) Concepts
  • Урок 40. 00:10:58
    Linear SVM Classification
  • Урок 41. 00:05:04
    Polynomial Kernel
  • Урок 42. 00:08:18
    Radial Basis Function
  • Урок 43. 00:08:05
    Support Vector Regression
  • Урок 44. 00:06:28
    Introduction to Decision Tree
  • Урок 45. 00:06:39
    Training and Visualizing a Decision Tree
  • Урок 46. 00:08:07
    Visualizing Boundary
  • Урок 47. 00:05:09
    Tree Regression, Regularization and Over Fitting
  • Урок 48. 00:04:50
    End to End Modeling
  • Урок 49. 00:24:02
    Project HR
  • Урок 50. 00:10:08
    Project HR with Google Colab
  • Урок 51. 00:04:58
    Ensemble Learning Methods Introduction
  • Урок 52. 00:20:59
    Bagging
  • Урок 53. 00:10:14
    Random Forests and Extra-Trees
  • Урок 54. 00:06:32
    AdaBoost
  • Урок 55. 00:03:01
    Gradient Boosting Machine
  • Урок 56. 00:02:46
    XGBoost Installation
  • Урок 57. 00:04:41
    XGBoost
  • Урок 58. 00:08:10
    Project HR - Human Resources Analytics
  • Урок 59. 00:06:23
    Ensemble of Ensembles Part 1
  • Урок 60. 00:04:54
    Ensemble of ensembles Part 2
  • Урок 61. 00:09:55
    kNN Introduction
  • Урок 62. 00:08:51
    Project Cancer Detection
  • Урок 63. 00:20:13
    Project Cancer Detection Part 1
  • Урок 64. 00:04:39
    Dimensionality Reduction Concept
  • Урок 65. 00:07:18
    PCA Introduction
  • Урок 66. 00:06:27
    Project Wine
  • Урок 67. 00:05:35
    Kernel PCA
  • Урок 68. 00:03:31
    Kernel PCA Demo
  • Урок 69. 00:05:37
    LDA vs PCA
  • Урок 70. 00:03:55
    Project Abalone
  • Урок 71. 00:15:56
    Clustering
  • Урок 72. 00:09:04
    k_Means Clustering
  • Урок 73. 00:21:31
    Estimating Simple Function with Neural Networks
  • Урок 74. 00:07:02
    Neural Network Architecture
  • Урок 75. 00:21:20
    Motivational Example - Project MNIST
  • Урок 76. 00:10:21
    Binary Classification Problem
  • Урок 77. 00:11:00
    Natural Language Processing - Binary Classification
  • Урок 78. 00:02:32
    Introduction to Neural Networks
  • Урок 79. 00:04:22
    Differences between Classical Programming and Machine Learning
  • Урок 80. 00:10:40
    Learning Representations
  • Урок 81. 00:19:11
    What is Deep Learning
  • Урок 82. 00:11:10
    Learning Neural Networks
  • Урок 83. 00:02:52
    Why Now?
  • Урок 84. 00:04:45
    Building Block Introduction
  • Урок 85. 00:04:00
    Tensors
  • Урок 86. 00:17:20
    Tensor Operations
  • Урок 87. 00:11:56
    Gradient Based Optimization
  • Урок 88. 00:04:29
    Getting Started with Neural Network and Deep Learning Libraries
  • Урок 89. 00:10:08
    Categories of Machine Learning
  • Урок 90. 00:15:23
    Over and Under Fitting
  • Урок 91. 00:04:55
    Machine Learning Workflow
  • Урок 92. 00:03:57
    Outline
  • Урок 93. 00:08:15
    Neural Network Revision
  • Урок 94. 00:07:32
    Motivational Example
  • Урок 95. 00:14:16
    Visualizing CNN
  • Урок 96. 00:05:55
    Understanding CNN
  • Урок 97. 00:05:51
    Layer - Input
  • Урок 98. 00:17:16
    Layer - Filter
  • Урок 99. 00:06:42
    Activation Function
  • Урок 100. 00:11:12
    Pooling, Flatten, Dense
  • Урок 101. 00:13:52
    Training Your CNN 1
  • Урок 102. 00:18:36
    Training Your CNN 2
  • Урок 103. 00:01:26
    Loading Previously Trained Model
  • Урок 104. 00:09:09
    Model Performance Comparison
  • Урок 105. 00:02:48
    Data Augmentation
  • Урок 106. 00:11:04
    Transfer Learning
  • Урок 107. 00:11:16
    Feature Extraction
  • Урок 108. 00:05:11
    State of the Art Tools
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