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    Курс: AI For Everyone. Чтобы вернуться, нажмите здесь.
    • Week 1 Introduction
    • Machine Learning
    • What is data?
    • The terminology of AI
    • What makes an AI company?
    • What machine learning can and cannot do
    • More examples of what machine learning can and cannot do
    • Non-technical explanation of deep learning (Part 1, optional)
    • Non-technical explanation of deep learning (Part 2, optional)
    • Week 2 Introduction
    • Workflow of a machine learning project
    • Workflow of a data science project
    • Every job function needs to learn how to use data
    • How to choose an AI project (Part 1)
    • How to choose an AI project (Part 2)
    • Working with an AI team
    • Technical tools for AI teams (optional)
    • Week 3 Introduction
    • Case study: Smart speaker
    • Case study: Self-driving car
    • Example roles of an AI team
    • AI Transformation Playbook (Part 1)
    • AI Transformation Playbook (Part 2)
    • AI pitfalls to avoid
    • Taking your first step in AI
    • Survey of major AI application areas (optional)
    • Survey of major AI techniques (optional)
    • Week 4 Introduction
    • A realistic view of AI
    • Discrimination / Bias
    • Adversarial attacks on AI
    • Adverse uses of AI
    • AI and developing economies
    • AI and jobs
    • Conclusion