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In this course, you will :
- This course gives students a broad overview of AI as well as a foundational understanding of what AI is, what it is not, and why it matters.
- The key distinctions between building a prediction engine with human-crafted rules and machine learning - and why this distinction is critical to AI.
- Three key AI capabilities, why they matter, and what AI applications cannot yet do.
- The types of data that AI applications feed on, where that data comes from, and how AI applications turn this data into 'intelligence' with the help of ML.
- The fundamental concepts underlying the machine learning and deep learning approaches that are powering the current wave of AI applications.
- Deep learning and artificial neural networks: the reality behind the hype
- Three major risk drivers associated with AI, why they arise, and their potential consequences in the workplace.
- An overview of how AI applications are developed – and who develops them (with the help of extended analogy).
- Why one of the most serious issues confronting the AI industry today - a significant skills gap - represents an opportunity for students.
- How to apply their own knowledge, skills, and expertise to contribute to AI projects.
- Students will learn how to build on the foundations they established in this course in order to transition from informed observer to valuable contributor.
Syllabus :
- Demystifying AI
- Building a prediction engine
- New capabilities... and limitations
- From data to 'intelligence'
- Machine learning approaches
- Risks and trade-offs
- How it's built
- The importance of domain expertise
