• Урок 1. 00:03:56
    What Does the Course Cover?
  • Урок 2. 00:00:51
    Setting Up the Environment - An Introduction (Do Not Skip, Please)!
  • Урок 3. 00:04:54
    Why Python and Why Jupyter?
  • Урок 4. 00:03:04
    Installing Anaconda
  • Урок 5. 00:02:28
    The Jupyter Dashboard - Part 1
  • Урок 6. 00:05:15
    The Jupyter Dashboard - Part 2
  • Урок 7. 00:01:19
    Installing sklearn
  • Урок 8. 00:01:28
    Introduction to Regression Analysis
  • Урок 9. 00:05:51
    The Linear Regression Model
  • Урок 10. 00:01:44
    Correlation vs Regression
  • Урок 11. 00:01:26
    Geometrical Representation
  • Урок 12. 00:04:40
    Python Packages Installation
  • Урок 13. 00:07:12
    Simple Linear Regression in Python
  • Урок 14. 00:01:22
    What is Seaborn?
  • Урок 15. 00:05:48
    What Does the StatsModels Summary Regression Table Tell us?
  • Урок 16. 00:03:38
    SST, SSR, and SSE
  • Урок 17. 00:03:14
    The Ordinary Least Squares (OLS)
  • Урок 18. 00:05:31
    Goodness of Fit: The R-Squared
  • Урок 19. 00:02:56
    The Multiple Linear Regression Model
  • Урок 20. 00:06:01
    Adjusted R-Squared
  • Урок 21. 00:02:02
    F-Statistic and F-Test for a Linear Regression
  • Урок 22. 00:02:22
    Assumptions of the OLS Framework
  • Урок 23. 00:01:51
    A1: Linearity
  • Урок 24. 00:04:10
    A2: No Endogeneity
  • Урок 25. 00:05:48
    A3: Normality and Homoscedasticity
  • Урок 26. 00:03:32
    A4: No Autocorrelation
  • Урок 27. 00:03:27
    A5: No Multicollinearity
  • Урок 28. 00:06:44
    Dealing with Categorical Data
  • Урок 29. 00:03:30
    Making Predictions
  • Урок 30. 00:02:15
    What is sklearn?
  • Урок 31. 00:01:57
    Game Plan for sklearn
  • Урок 32. 00:05:39
    Simple Linear Regression with sklearn
  • Урок 33. 00:04:50
    Simple Linear Regression with sklearn - Summary Table
  • Урок 34. 00:03:11
    Multiple Linear Regression with sklearn
  • Урок 35. 00:04:46
    Adjusted R-Squared
  • Урок 36. 00:04:42
    Feature Selection through p-values (F-regression)
  • Урок 37. 00:02:11
    Creating a Summary Table with the p-values
  • Урок 38. 00:05:39
    Feature Scaling
  • Урок 39. 00:05:23
    Feature Selection through Standardization
  • Урок 40. 00:03:54
    Making Predictions with Standardized Coefficients
  • Урок 41. 00:02:43
    Underfitting and Overfitting
  • Урок 42. 00:06:55
    Training and Testing
  • Урок 43. 00:12:00
    Practical Example (Part 1)
  • Урок 44. 00:06:13
    Practical Example (Part 2)
  • Урок 45. 00:03:16
    Practical Example (Part 3)
  • Урок 46. 00:08:11
    Practical Example (Part 4)
  • Урок 47. 00:07:35
    Practical Example (Part 5)
  • Урок 48. 00:01:20
    Introduction to Logistic Regression
  • Урок 49. 00:04:43
    A Simple Example of a Logistic Regression in Python
  • Урок 50. 00:04:01
    What is the Difference Between a Logistic and a Logit Function?
  • Урок 51. 00:02:49
    Your First Logistic Regression
  • Урок 52. 00:02:27
    A Coding Tip (optional)
  • Урок 53. 00:04:07
    Going through the Regression Summary Table
  • Урок 54. 00:04:31
    Interpreting the Odds Ratio
  • Урок 55. 00:04:33
    Dummies in a Logistic Regression
  • Урок 56. 00:03:22
    Assessing the Accuracy of a Classification Model
  • Урок 57. 00:03:44
    Underfitting and Overfitting
  • Урок 58. 00:05:06
    Testing our Model and Bulding a Confusion Matrix
  • Урок 59. 00:03:42
    Introduction to Cluster Analysis
  • Урок 60. 00:04:32
    Examples of Clustering
  • Урок 61. 00:02:33
    Classification vs Clustering
  • Урок 62. 00:03:20
    Math Concepts Needed to Proceed
  • Урок 63. 00:04:42
    K-Means Clustering
  • Урок 64. 00:07:49
    A Hands on Example of K-Means
  • Урок 65. 00:02:51
    Categorical Data in Cluster Analysis
  • Урок 66. 00:06:12
    The Elbow Method or How to Choose the Number of Clusters
  • Урок 67. 00:03:24
    Pros and Cons of K-Means
  • Урок 68. 00:04:33
    Standardization of Features when Clustering
  • Урок 69. 00:01:32
    Cluster Analysis and Regression Analysis
  • Урок 70. 00:06:04
    Practical Example: Market Segmentation (Part 1)
  • Урок 71. 00:06:59
    Practical Example: Market Segmentation (Part 2)
  • Урок 72. 00:04:48
    What Can be Done with Cluster Analysis?
  • Урок 73. 00:03:40
    Other Types of Clustering
  • Урок 74. 00:05:22
    The Dendrogram
  • Урок 75. 00:04:35
    Heatmaps
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