AI foundations for business professionals

A code-free intro to artificial intelligence, ML, & data science for professionals, marketers, managers, & executives
Free tutorial
Rating: 4.3 out of 5 (171 ratings)
5,418 students
2hr of on-demand video
English [Auto]

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.


  • None whatsoever. This course is designed to help complete beginners in the field of AI make the transition to informed participants in the workplace.


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

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


Chief Data Scientist, FluentInAI.com | Teaching AI literacy
Marshall Lincoln
  • 4.3 Instructor Rating
  • 171 Reviews
  • 5,418 Students
  • 1 Course

As a practitioner of data science, Marshall has led business transformation projects and hired, managed and worked alongside data science teams. He works with international blue-chip organizations and has authored incisive research into the responsible and effective use of AI.

Marshall is Co-author and Chief Data Scientist at FluentinAI[dot]com. Fluent in AI is an online data science course designed to help non-technical business stakeholders find a common language with the technical teams they partner with, to deliver effective AI and ML solutions at their organizations.

Marshall is Co-founder of the Lucid Analytics Project - an Impact research consultancy which looks at cross-industry applications of AI, combining experience and interviews with practitioners and risk managers, data scientists, technology providers, consultants, regulators, and academics to produce long-form research.

He is also the founder and CEO of BHN Ventures (a management consulting firm focused on the intersection of applied data science, customer data platforms, and digital marketing)

Marshall's prior experience has equipped him with a deep and comprehensive understanding of the end-to-end business intelligence function (especially at SaaS companies): process, tools, methodologies, technologies, objectives

Co-author, FluentInAI.com
Keyur Patel
  • 4.3 Instructor Rating
  • 171 Reviews
  • 5,418 Students
  • 1 Course

As an independent journalist and consultant, Keyur has written features and analysis for a number of leading international publishers. He has also produced independent research for dozens of organizations, including blue chip companies, academic bodies, and not-for-profits.

He is a research associate at the Centre for the Study of Financial Innovation - and has authored multiple reports for the Centre, which have been widely covered in the international media. He was formerly Marjorie Deane Fellow at the Financial Times and is a graduate in economics from University College London.

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