Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
AI foundations for business professionals
Rating: 4.3 out of 5(590 ratings)
8,533 students

AI foundations for business professionals

A code-free intro to artificial intelligence, ML, & data science for professionals, marketers, managers, & executives
Created byMarshall Lincoln, Keyur Patel
Last updated 3/2021
English

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 content

9 sections • 21 lectures • 1h 59m total length

  • A term with many definitions4:03
  • Introducing prediction engines5:04

    Explore how machine learning enables computers to perform tasks without explicit programming, infer instructions from data, and improve with experience using neural networks and deep learning for prediction.

  • It's not magic6:02

    Discover how prediction underpins modern AI, with pattern detection across text, images, video, and sensors driving better information and decision making in a wide range of business applications.

  • Module 1: Quiz

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:

---

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

Report abuse