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  1. Урок 1. 00:02:40
    Introduction to Regression Analysis
  2. Урок 2. 00:01:23
    Game Plan for Multilinear Regression
  3. Урок 3. 00:01:54
    CASE STUDY Briefing - Pricing Diamonds
  4. Урок 4. 00:05:13
    Linear Regression
  5. Урок 5. 00:02:28
    Python - Libraries and Data
  6. Урок 6. 00:03:19
    Python - Exploratory Data Analysis
  7. Урок 7. 00:02:17
    Python - Linear Regression
  8. Урок 8. 00:04:24
    Regression Statistics
  9. Урок 9. 00:07:08
    Python - Plotting Regression Curve
  10. Урок 10. 00:04:01
    Dummy Variable (Trap)
  11. Урок 11. 00:07:16
    Python - Linear Regression with Dummy Variables
  12. Урок 12. 00:09:36
    EXERCISE: Create Function that Reads the Regression Coefficients
  13. Урок 13. 00:04:02
    CASE STUDY - Linearity Bias - We Will All Be Obese! Wait What?
  14. Урок 14. 00:01:48
    Multilinear Regression
  15. Урок 15. 00:05:40
    Python - Categorical Variables
  16. Урок 16. 00:03:28
    Under and Overfitting
  17. Урок 17. 00:02:35
    Training and Test Set
  18. Урок 18. 00:03:45
    Python - Multilinear Regression
  19. Урок 19. 00:06:08
    Assessing Regression Models
  20. Урок 20. 00:03:48
    Python - Assessing Regression Model
  21. Урок 21. 00:02:52
    CASE STUDY - Dangers of Regression Analysis
  22. Урок 22. 00:02:03
    Multilinear Regression Wrap Up
  23. Урок 23. 00:01:20
    Captone Project - Understanding Sales Drivers
  24. Урок 24. 00:07:21
    Python - Solutions - Step 1
  25. Урок 25. 00:04:30
    Python - Solutions - Step 2-4
  26. Урок 26. 00:03:48
    Python - Solutions - Step 5-6
  27. Урок 27. 00:01:39
    Game Plan for Logistic Regression
  28. Урок 28. 00:01:26
    CASE STUDY Briefing - Spam Emails
  29. Урок 29. 00:03:29
    Logistic Regression
  30. Урок 30. 00:03:32
    Python - Preparing Script and Loading Data
  31. Урок 31. 00:03:45
    Python - Summary Statistics
  32. Урок 32. 00:05:37
    Python - Histograms and Outlier Detection
  33. Урок 33. 00:03:27
    Python - Correlation Matrix
  34. Урок 34. 00:04:00
    Python - Logistic Regression Preparation
  35. Урок 35. 00:02:12
    How to Read Logistic Regression Coefficients
  36. Урок 36. 00:02:18
    Python - Logistic Regression
  37. Урок 37. 00:09:07
    Python - Build a Coefficient Function with ChatGPT
  38. Урок 38. 00:03:20
    Python - Predictions
  39. Урок 39. 00:06:25
    Confusion Matrix and Model Assessment
  40. Урок 40. 00:05:35
    Python - Confusion Matrix and Classification Report
  41. Урок 41. 00:05:31
    Python - Assessing Classification Models with ChatGPT
  42. Урок 42. 00:03:16
    Section Wrap Up - Logistic Regression
  43. Урок 43. 00:01:03
    Capstone Project - Surviving Titanic
  44. Урок 44. 00:08:21
    Python - Libraries and Data
  45. Урок 45. 00:06:33
    Python - Removing Outliers and EDA
  46. Урок 46. 00:06:07
    Python - Logistic Regression Model and Assessment
  47. Урок 47. 00:02:15
    Game Plan for Cox Proportional Hazard Regression
  48. Урок 48. 00:07:48
    Introduction to Survival Analysis
  49. Урок 49. 00:01:48
    CASE STUDY - Briefing
  50. Урок 50. 00:05:10
    Python - Libraries and Data
  51. Урок 51. 00:04:36
    Kaplan-Meier Estimator
  52. Урок 52. 00:04:23
    Python - Kaplan Meier Estimator
  53. Урок 53. 00:02:47
    Python - Calculating for a Specific Event
  54. Урок 54. 00:03:52
    Python - Plotting Kaplan-Meier and Cumulated Curves
  55. Урок 55. 00:03:46
    Censoring
  56. Урок 56. 00:02:56
    Log Rank Test
  57. Урок 57. 00:05:51
    Python - Kaplan-Meier Estimator per Gender and Visualization
  58. Урок 58. 00:06:34
    Python - Log Rank Test
  59. Урок 59. 00:04:52
    Cox Proportional Hazard Regression
  60. Урок 60. 00:03:12
    Python - Prepare Data for CPH Model
  61. Урок 61. 00:09:37
    Python - Cox Proportional Hazard Regression
  62. Урок 62. 00:02:13
    Python - Visualize Results
  63. Урок 63. 00:05:19
    Assessing Cox Proportional Hazard Models
  64. Урок 64. 00:08:38
    Python - Assessing the CPH Model
  65. Урок 65. 00:03:39
    Python - Predicting Specific Instances
  66. Урок 66. 00:03:15
    Cox Proportional Hazard Regression Wrap Up
  67. Урок 67. 00:01:24
    Capstone Project - Will Your App Make it?
  68. Урок 68. 00:07:11
    Python - Libraries and Data
  69. Урок 69. 00:19:11
    Python - Data Cleaning
  70. Урок 70. 00:08:27
    Python - Dependent Variable
  71. Урок 71. 00:04:31
    Python - Kaplan-Meier Estimator
  72. Урок 72. 00:10:01
    Python - Cox Model
  73. Урок 73. 00:04:20
    Game Plan for Logarithmic Regression
  74. Урок 74. 00:04:06
    Python - Logarithmic Regression Setup
  75. Урок 75. 00:06:12
    Python - Data Prep and Visualization
  76. Урок 76. 00:04:43
    Python - Normal Linear Regression
  77. Урок 77. 00:04:00
    Python - Plotting Normal Linear Regression
  78. Урок 78. 00:05:43
    Python - Linear - Log Regression
  79. Урок 79. 00:05:43
    Python - Log - Linear Regression
  80. Урок 80. 00:06:37
    Python - Log - Binary
  81. Урок 81. 00:03:23
    Python - Log-Log Regression
  82. Урок 82. 00:00:52
    Let's Keep Learning Together!