• Урок 1. 00:03:20
    Introduction and Welcome Message
  • Урок 2. 00:10:44
    Introduction, Key Tips and Best Practices
  • Урок 3. 00:17:55
    Course Outline and Key Learning Outcomes
  • Урок 4. 00:02:51
    Project Introduction and Welcome Message
  • Урок 5. 00:11:16
    Task #1 - Understand the Problem Statement & Business Case
  • Урок 6. 00:12:26
    Task #2 - Import Libraries and Datasets
  • Урок 7. 00:09:36
    Task #3 - Perform Image Visualizations
  • Урок 8. 00:16:52
    Task #4 - Perform Images Augmentation
  • Урок 9. 00:07:45
    Task #5 - Perform Data Normalization and Scaling
  • Урок 10. 00:20:33
    Task #6 - Understand Artificial Neural Networks (ANNs) Theory & Intuition
  • Урок 11. 00:18:03
    Task #7 - Understand ANNs Training & Gradient Descent Algorithm
  • Урок 12. 00:13:01
    Task #8 - Understand Convolutional Neural Networks and ResNets
  • Урок 13. 00:12:46
    Task #9 - Build ResNet to Detect Key Facial Points
  • Урок 14. 00:07:41
    Task #10 - Compile and Train Facial Key Points Detector Model
  • Урок 15. 00:04:55
    Task #11 - Assess Trained ResNet Model Performance
  • Урок 16. 00:12:01
    Task #12 - Import and Explore Facial Expressions (Emotions) Datasets
  • Урок 17. 00:07:23
    Task #13 - Visualize Images for Facial Expression Detection
  • Урок 18. 00:13:32
    Task #14 - Perform Image Augmentation
  • Урок 19. 00:14:58
    Task #15 - Build & Train a Facial Expression Classifier Model
  • Урок 20. 00:14:14
    Task #16 - Understand Classifiers Key Performance Indicators (KPIs)
  • Урок 21. 00:13:36
    Task #17 - Assess Facial Expression Classifier Model
  • Урок 22. 00:07:38
    Task #18 - Make Predictions from Both Models 1. Key Facial Points & 2. Emotion
  • Урок 23. 00:10:02
    Task #19 - Save Trained Model for Deployment
  • Урок 24. 00:04:25
    Task #20 - Serve Trained Model in TensorFlow 2.0 Serving
  • Урок 25. 00:08:24
    Task #21 - Deploy Both Models and Make Inference
  • Урок 26. 00:02:41
    Project Introduction and Welcome Message
  • Урок 27. 00:16:35
    Task #1 - Understand the Problem Statement and Business Case
  • Урок 28. 00:11:38
    Task #2 - Import Libraries and Datasets
  • Урок 29. 00:20:45
    Task #3 - Visualize and Explore Datasets
  • Урок 30. 00:10:38
    Task #4 - Understand the Intuition behind ResNet and CNNs
  • Урок 31. 00:11:51
    Task #5 - Understand Theory and Intuition Behind Transfer Learning
  • Урок 32. 00:21:08
    Task #6 - Train a Classifier Model To Detect Brain Tumors
  • Урок 33. 00:09:05
    Task #7 - Assess Trained Classifier Model Performance
  • Урок 34. 00:13:24
    Task #8 - Understand ResUnet Segmentation Models Intuition
  • Урок 35. 00:14:21
    Task #9 - Build a Segmentation Model to Localize Brain Tumors
  • Урок 36. 00:04:06
    Task #10 - Train ResUnet Segmentation Model
  • Урок 37. 00:12:28
    Task #11 - Assess Trained ResUNet Segmentation Model Performance
  • Урок 38. 00:02:11
    Project Introduction and Welcome Message
  • Урок 39. 00:07:18
    Task #1 - Understand AI Applications in Marketing
  • Урок 40. 00:13:51
    Task #2 - Import Libraries and Datasets
  • Урок 41. 00:16:47
    Task #3 - Perform Exploratory Data Analysis (Part #1)
  • Урок 42. 00:19:18
    Task #4 - Perform Exploratory Data Analysis (Part #2)
  • Урок 43. 00:16:58
    Task #5 - Understand Theory and Intuition Behind K-Means Clustering Algorithm
  • Урок 44. 00:08:48
    Apply Elbow Method to Find the Optimal Number of Clusters
  • Урок 45. 00:15:55
    Task #7 - Apply K-Means Clustering Algorithm
  • Урок 46. 00:10:33
    Task #8 - Understand Intuition Behind Principal Component Analysis (PCA)
  • Урок 47. 00:08:40
    Task #9 - Understand the Theory and Intuition Behind Auto-encoders
  • Урок 48. 00:13:05
    Task #10 - Apply Auto-encoders and Perform Clustering
  • Урок 49. 00:02:40
    Project Introduction and Welcome Message
  • Урок 50. 00:11:02
    Task #1 - Understand the Problem Statement & Business Case
  • Урок 51. 00:04:47
    Task #2 - Import Libraries and Datasets
  • Урок 52. 00:20:44
    Task #3 - Visualize and Explore Dataset
  • Урок 53. 00:06:03
    Task #4 - Clean Up the Data
  • Урок 54. 00:20:46
    Task #5 - Understand the Theory & Intuition Behind XG-Boost Algorithm
  • Урок 55. 00:19:50
    Task #6 - Understand XG-Boost Algorithm Key Steps
  • Урок 56. 00:07:54
    Task #7 - Train XG-Boost Algorithm Using Scikit-Learn
  • Урок 57. 00:06:58
    Task #8 - Perform Grid Search and Hyper-parameters Optimization
  • Урок 58. 00:07:16
    Task #9 - Understand XG-Boost in AWS SageMaker
  • Урок 59. 00:14:26
    Task #10 - Train XG-Boost in AWS SageMaker
  • Урок 60. 00:09:43
    Task #11 - Deploy Model and Make Inference
  • Урок 61. 00:13:11
    Task #12 - Train and Deploy Model Using AWS AutoPilot (Minimal Coding Required!)
  • Урок 62. 00:01:47
    Project Introduction and Welcome Message
  • Урок 63. 00:11:14
    Task #1 - Understand the Problem Statement & Business Case
  • Урок 64. 00:07:07
    Task #2 - Import Model with Pre-trained Weights
  • Урок 65. 00:09:08
    Task #3 - Import and Merge Images
  • Урок 66. 00:09:45
    Task #4 - Run the Pre-trained Model and Explore Activations
  • Урок 67. 00:19:28
    Task #5 - Understand the Theory & Intuition Behind Deep Dream Algorithm
  • Урок 68. 00:05:38
    Task #6 - Understand The Gradient Operations in TF 2.0
  • Урок 69. 00:09:11
    Task #7 - Implement Deep Dream Algorithm Part #1
  • Урок 70. 00:10:27
    Task #8 - Implement Deep Dream Algorithm Part #2
  • Урок 71. 00:06:46
    Task #9 - Apply DeepDream Algorithm to Generate Images
  • Урок 72. 00:07:21
    Task #10 - Generate DeepDream Video
  • Урок 73. 00:01:51
    Project Introduction and Welcome Message
  • Урок 74. 00:08:54
    What is AWS and Cloud Computing
  • Урок 75. 00:09:26
    Key Machine Learning Components and AWS Tour
  • Урок 76. 00:06:20
    Regions and Availability Zones
  • Урок 77. 00:14:33
    Amazon S3
  • Урок 78. 00:12:42
    EC2 and Identity and Access Management (IAM)
  • Урок 79. 00:05:48
    AWS Free Tier Account Setup and Overview
  • Урок 80. 00:09:15
    AWS SageMaker Overview
  • Урок 81. 00:10:47
    AWS SageMaker Walk-through
  • Урок 82. 00:08:42
    AWS SageMaker Studio Overview
  • Урок 83. 00:07:00
    AWS SageMaker Studio Walk-through
  • Урок 84. 00:11:04
    AWS SageMaker Model Deployment
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