AI foundations for business professionals

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

Language: english

Note: 4.7/5 (38 notes) 1,948 students

Instructor(s): Marshall Lincoln

Last update: 2021-03-14

What you’ll learn

• This course provides students with a broad introduction to AI. Students will be equipped with 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 machine learning – 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 and where to build upon the foundations they learned upon in this course, to make the move from informed observer to valuable contributor.

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

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

Course content

• Demystifying AI
  • A term with many definitions
  • Introducing prediction engines
  • It’s not magic
  • Module 1: Quiz
• Building a prediction engine
  • What characterizes AI?
  • Two approaches compared: a gentle introduction
  • Human-crafted rules or machine learning?
  • Module 2: Quiz
• New capabilities… and limitations
  • Three new capabilities
  • What can’t AI applications do well?
  • Module 3: Quiz
• From data to ‘intelligence’
  • Inputs: what is data?
  • Outputs: predictions and automated instructions
  • Module 4: Quiz
• Machine learning approaches
  • Machine learning – defined
  • Algorithms and models
  • Supervised and unsupervised learning
  • Artificial neural networks and deep learning
  • Module 5: Quiz
• Risks and trade-offs
  • Three drivers of new risks
  • What could go wrong? Potential consequences
  • Module 6: Quiz
• How it’s built
  • Oil and data: two similar transformations
  • Who builds AI applications?
  • Module 7: Quiz
• The importance of domain expertise
  • The skills gap
  • What do you know that data scientists might not?
• Bonus module: Go from observer to contributor
  • Go from observer to contributor