AI foundations for business professionals


AI foundations for business professionals , A code-free intro to artificial intelligence, ML, & data science for professionals, marketers, managers, & executives.

What you”ll learn:

  • 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.

Course Description

Full course outline:

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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