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Премиум
  1. Урок 1. 00:04:04
    Introduction
  2. Урок 2. 00:02:53
    Course Overview
  3. Урок 3. 00:03:05
    Growing Importance of an AI & Data PM
  4. Урок 4. 00:04:49
    The Role of a Product Manager
  5. Урок 5. 00:03:26
    Differentiation of a PM in AI & Data
  6. Урок 6. 00:04:04
    Product Management vs. Project Management
  7. Урок 7. 00:03:50
    A Product Manager as an Analytics Translator
  8. Урок 8. 00:03:00
    Data Analysis vs. Data Science
  9. Урок 9. 00:05:49
    A Traditional Algorithm vs. AI
  10. Урок 10. 00:06:03
    Explaining Machine Learning
  11. Урок 11. 00:05:16
    Explaining Deep Learning
  12. Урок 12. 00:06:04
    When to use Machine Learning vs. Deep Learning
  13. Урок 13. 00:04:54
    Supervised, Unsupervised, & Reinforcement Learning
  14. Урок 14. 00:04:55
    AI Business Model Innovations
  15. Урок 15. 00:04:00
    When to Use AI
  16. Урок 16. 00:03:32
    SWOT Analysis
  17. Урок 17. 00:04:12
    Building a Hypothesis
  18. Урок 18. 00:03:47
    Testing a Hypothesis
  19. Урок 19. 00:04:02
    AI Business Canvas
  20. Урок 20. 00:03:59
    User Experience for Data & AI
  21. Урок 21. 00:04:20
    Getting to the Core Problem
  22. Урок 22. 00:04:28
    User Research Methods
  23. Урок 23. 00:04:21
    Developing User Personas
  24. Урок 24. 00:04:26
    Prototyping with AI
  25. Урок 25. 00:05:38
    Data Growth Strategy
  26. Урок 26. 00:02:59
    Open Data
  27. Урок 27. 00:03:10
    Company Data
  28. Урок 28. 00:06:45
    Crowdsourcing Labeled Data
  29. Урок 29. 00:04:17
    New Feature Data
  30. Урок 30. 00:03:30
    Acquisition/Purchase Data Collection
  31. Урок 31. 00:03:51
    Databases, Data Warehouses, & Data Lakes
  32. Урок 32. 00:03:23
    AI Flywheel Effect
  33. Урок 33. 00:03:16
    Top & Bottom Problem Solving
  34. Урок 34. 00:04:28
    Product Ideation Techniques
  35. Урок 35. 00:05:48
    Complexity vs. Benefit Prioritization
  36. Урок 36. 00:05:45
    MVPs & MVDs (Minimum Viable Data)
  37. Урок 37. 00:04:55
    Agile & Data Kanban
  38. Урок 38. 00:05:08
    Who Should Buid Your Model
  39. Урок 39. 00:04:31
    Enterpise AI
  40. Урок 40. 00:04:33
    Machine Learning as a Service (MLaaS)
  41. Урок 41. 00:03:27
    In-House AI & The Machine Learning Lifecycle
  42. Урок 42. 00:04:43
    Timelines & Diminishing Returns
  43. Урок 43. 00:04:40
    Setting a Model Performance Metric
  44. Урок 44. 00:04:22
    Dividing Test Data
  45. Урок 45. 00:03:16
    The Confusion Matrix
  46. Урок 46. 00:03:49
    Precision, Recall & F1 Score
  47. Урок 47. 00:06:24
    Optimizing for Experience
  48. Урок 48. 00:03:47
    Error Recovery
  49. Урок 49. 00:05:35
    Model Deployment Methods
  50. Урок 50. 00:04:21
    Monitoring Models
  51. Урок 51. 00:03:50
    Selecting a Feedback Metric
  52. Урок 52. 00:03:47
    User Feedback Loops
  53. Урок 53. 00:03:17
    Shadow Deployments
  54. Урок 54. 00:04:56
    AI Hierarchy of Needs
  55. Урок 55. 00:04:21
    AI Within an Organization
  56. Урок 56. 00:04:54
    Roles in AI & Data Teams
  57. Урок 57. 00:03:19
    Managing Team Workflow
  58. Урок 58. 00:04:07
    Dual & Triple-Track Agile
  59. Урок 59. 00:05:05
    Internal Stakeholder Management
  60. Урок 60. 00:04:51
    Setting Data Expectations
  61. Урок 61. 00:04:20
    Active Listening & Communication
  62. Урок 62. 00:04:19
    Compelling Presentations with Storytelling
  63. Урок 63. 00:04:58
    Running Effective Meetings
  64. Урок 64. 00:03:39
    AI User Concerns
  65. Урок 65. 00:05:11
    Bad Actors & Security
  66. Урок 66. 00:05:48
    AI Amplifying Human Bias
  67. Урок 67. 00:04:01
    Data Laws & Regulations