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  1. Урок 1. 00:01:05
    Updates on Udemy Reviews
  2. Урок 2. 00:03:44
    Introduction
  3. Урок 3. 00:09:06
    Key Tips and Best Practices
  4. Урок 4. 00:14:57
    Course Outline and Key Learning Outcomes
  5. Урок 5. 00:02:33
    Introduction to Case Study and Key Learning Outcomes
  6. Урок 6. 00:09:22
    Task #1: Problem Statement and Business Case
  7. Урок 7. 00:12:45
    Task #2: Import Libraries and Datasets
  8. Урок 8. 00:13:32
    Task #3: Explore Dataset - Part 1
  9. Урок 9. 00:09:45
    Task #3: Explore Dataset - Part 2
  10. Урок 10. 00:08:48
    Task #3: Explore Dataset - Part 3
  11. Урок 11. 00:09:46
    Task #3: Explore Dataset - Part 4
  12. Урок 12. 00:09:39
    Task #4: Perform Data Cleaning
  13. Урок 13. 00:15:26
    Task #5: Understand intuition of Random Forest, Logistic Regression, and ANNs
  14. Урок 14. 00:12:35
    Task #6: Understand Classification KPIs
  15. Урок 15. 00:07:54
    Task #7: Build and Train Logistic Regression Classifier
  16. Урок 16. 00:02:58
    Task #8: Build and Train Random Forest Classifier Model
  17. Урок 17. 00:10:26
    Task #9: Build and Train Artificial Neural Network Classifier Model
  18. Урок 18. 00:02:05
    Introduction to Case Study and Key Learning Outcomes
  19. Урок 19. 00:10:42
    Task #1: Understand Problem Statement and Business Case
  20. Урок 20. 00:14:43
    Task #2: Import Libraries and Datasets
  21. Урок 21. 00:19:56
    Task #3: Perform Data Visualization
  22. Урок 22. 00:15:19
    Task #4: Understand the Theory and Intuition behind K-Mean Algorithm
  23. Урок 23. 00:08:11
    Task #5: Obtain Optimal Number of Clusters "K"
  24. Урок 24. 00:09:38
    Task #6: Apply K-Means Clustering to Perform Market Segmentation
  25. Урок 25. 00:10:07
    Task #7: Understand the Intuition Behind Principal Component Analysis (PCA)
  26. Урок 26. 00:07:51
    Task #8: Understand the Intuition Behind Autoencoders
  27. Урок 27. 00:12:08
    Task #9: Build and Train Autoencoder - Part #1
  28. Урок 28. 00:14:03
    Build and Train Autoencoder - Part #2
  29. Урок 29. 00:01:32
    Introduction to Case Study and Key Learning Outcomes
  30. Урок 30. 00:11:52
    Task #1: Understand the Problem Statement and Business Case
  31. Урок 31. 00:11:18
    Task #2: Import Datasets - Part #1
  32. Урок 32. 00:05:40
    Task #2: Import Datasets - Part #2
  33. Урок 33. 00:12:22
    Task #3: Explore Data - Part #1
  34. Урок 34. 00:11:12
    Task #3: Explore Data - Part #2
  35. Урок 35. 00:08:29
    Task #3: Explore Data - Part #3
  36. Урок 36. 00:12:57
    Task #3: Explore Data - Part #4
  37. Урок 37. 00:06:09
    Task #4: Understand Facebook Prophet intuition
  38. Урок 38. 00:10:30
    Task #5: Train The Model - Part #1
  39. Урок 39. 00:12:24
    Task #6: Train The Model - Part #2
  40. Урок 40. 00:02:14
    Introduction to Case Study and Key Learning Outcomes
  41. Урок 41. 00:08:06
    Task #1: Understand the Business Case and Problem Statement
  42. Урок 42. 00:16:41
    Task #2: Load and Explore Dataset
  43. Урок 43. 00:06:11
    Task #3: Visualize Datasets
  44. Урок 44. 00:13:57
    Task #4: Understand Intuition Behind Convolutional Neural Networks (CNNs)
  45. Урок 45. 00:09:54
    Task #5: Understand Intuition Behind Transfer Learning
  46. Урок 46. 00:05:10
    Task #6: Load Model with Pretrained Weights
  47. Урок 47. 00:20:09
    Task #7: Build and Train ResNet
  48. Урок 48. 00:15:22
    Task #8: Evaluate Trained Model Performance
  49. Урок 49. 00:01:50
    Introduction to Case Study and Key Learning Outcomes
  50. Урок 50. 00:05:48
    Task #1: Understand Problem Statement and Business Case
  51. Урок 51. 00:07:20
    Task #2: Import Libraries and Datasets
  52. Урок 52. 00:09:59
    Task #3: Explore Dataset - Part #1
  53. Урок 53. 00:14:41
    Task #3: Explore Dataset - Part #2
  54. Урок 54. 00:06:41
    Task #4: Perform Data Cleaning
  55. Урок 55. 00:05:15
    Task #5: Remove Punctuation
  56. Урок 56. 00:07:57
    Task #6: Remove Stopwords
  57. Урок 57. 00:09:19
    Task #7: Perform Tokenization/Count Vectorization
  58. Урок 58. 00:13:46
    Task #8: Perform Text Cleaning pipeline
  59. Урок 59. 00:17:52
    Task #9: Naive Bayes Intuition
  60. Урок 60. 00:04:02
    Task #10: Train a Naive Bayes Classifier
  61. Урок 61. 00:07:41
    Task #11: Evaluate Trained Naive Bayes Classifier
  62. Урок 62. 00:06:10
    Task #12: Train and Evaluate a Logistic Regression Classifier
  63. Урок 63. 00:05:57
    Introduction and Welcome Message
  64. Урок 64. 00:09:05
    Task #1 - Understand the Problem Statement & Business Case
  65. Урок 65. 00:11:23
    Task #2 - Import Libraries and Datasets
  66. Урок 66. 00:17:14
    Task #3 - Visualize and Explore Dataset
  67. Урок 67. 00:09:11
    Task #4 - Understand the Intuition behind ResNet, CNNs, and Transfer Learning
  68. Урок 68. 00:10:59
    Task #5 - Build & Train ResNet Classifiers
  69. Урок 69. 00:05:44
    Task #6 - Assess Trained ResNet Model Performance
  70. Урок 70. 00:10:48
    Task #7 - Understand the Intuition behind ResUnet Segmentation Models
  71. Урок 71. 00:11:44
    Task #8 - Build & Train a ResUnet Segmentation Model
  72. Урок 72. 00:10:15
    Task #9 - Assess Trained ResUnet Model