AI foundations for business professionals-Course
This course provides students with a broad introduction to AI, and a foundational understanding of what AI is, what it is not, and why it matters.
The main differences between building a prediction engine using human-crafted rules and machine learning - and why this difference is central to AI.
Three key capabilities that AI makes possible, 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 - with the help of ML - turn this data into 'intelligence'.
The main principles behind the machine learning and deep learning approaches that power the current wave of AI applications.
Artificial neural networks and deep learning: the reality behind the hype.
Three main drivers of risks which are characteristic of AI, why they arise, and their potential consequences in a workplace environment.
An overview of how AI applications are built - and who builds them (with the help of extended analogy).
Why one of the biggest problems the AI industry faces today - a pronounced skills gap - represents an opportunity for students.
How to use their own knowledge, skills and expertise to provide valuable contributions to AI projects.
Students will learn how to build upon the foundations they learned upon in this course, to make the move from informed observer to valuable contributor.
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Who this course is for:
- This course is accessible to anybody. I has been designed with a special focus on the requirements and objectives generally shared by individuals with the following roles:
- Executives
- Board members
- Line of business managers
- Analysts
- Marketers
- Other business professionals who want to engage with AI projects
- Students and anyone contemplating a future in data science
Requirements
- None whatsoever. This course is designed to help complete beginners in the field of AI make the transition to informed participants in the workplace.
Description
Full course outline:
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Module 1: Demystifying AI
Lecture 1
- A term with any definitions
- An objective and a field
- Excitement and disappointment
Lecture 2:
- Introducing prediction engines
- Introducing machine learning
Lecture 3
- Prediction engines
- Don't expect 'intelligence' (It's not magic)
Module 2: Building a prediction engine
Lecture 4:
- What characterizes AI? Inputs, model, outputs
Lecture 5:
- Two approaches compared: a gentle introduction
- Building a jacket prediction engine
Lecture 6:
- Human-crafted rules or machine learning?
Module 3: New capabilities... and limitations
Lecture 7
- Expanding the number of tasks that can be automated
- New insights --> more informed decisions
- Personalization: when predictions are granular... and cheap
Lecture 8:
- What can't AI applications do well?
Module 4: From data to 'intelligence
Lecture 9
- What is data?
- Structured data
- Machine learning unlocks new insights from more types of data
Lecture 10
- What do AI applications do?
- Predictions and automated instructions
- When is a machine 'decision' appropriate?
Module 5: Machine learning approaches
Lecture 11
- Three definitions
Machine learning basics
Lecture 12
- What's an algorithm?
- Traditional vs machine learning algorithms
- What's a machine learning model?
Lecture 13
- Machine learning approaches
- Supervised learning
- Unsupervised learning
Lecture 14
- Artificial neural networks and deep learning
Module 6: Risks and trade-offs
Lecture 15:
- Beware the hype
- Three drivers of new risks
Lecture 16
- What could go wrong? Potential consequences
Module 7: How it's built
Lecture 17
- It's all about data
Oil and data: two similar transformations
Lecture 18
- The anatomy of an AI project
- The data scientist's mission
Module 8: The importance of domain expertise
Lecture 19:
- The skills gap
- A talent gap and a knowledge gap
- Marrying technical sills and domain expertise
Lecture 20: What do you know that data scientists might not?
- Applying your skills to AI projects
- What might you know that data scientists' not?
- How can you leverage your expertise?
Module 9: Bonus module: Go from observer to contributor
Lecture 21
- Go from observer to contributor
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