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