AI foundations for business professionals

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

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.

 

AI foundations for business professionalsAI foundations for business professionals

 

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

 

AI foundations for business professionalsAI foundations for business professionals

 

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

 


 

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