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AI foundations for business professionals

IT & Software

Rating4.4
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AI foundations for business professionals, available at Free, has an average rating of 4.43, with 21 lectures, 7 quizzes, based on 247 reviews, and has 5882 subscribers.

You will learn about 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. This course is ideal for individuals who are 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: or Executives or Board members or Line of business managers or Analysts or Marketers or Other business professionals who want to engage with AI projects or Students and anyone contemplating a future in data science It is particularly useful 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: or Executives or Board members or Line of business managers or Analysts or Marketers or Other business professionals who want to engage with AI projects or Students and anyone contemplating a future in data science.

Enroll now: AI foundations for business professionals

Summary

Title: AI foundations for business professionals

Price: Free

Average Rating: 4.43

Number of Lectures: 21

Number of Quizzes: 7

Number of Published Lectures: 21

Number of Published Quizzes: 7

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 28

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

  • 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

Target Audiences

  • 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

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

Course Curriculum

Chapter 1: Demystifying AI

Lecture 1: A term with many definitions

Lecture 2: Introducing prediction engines

Lecture 3: It's not magic

Chapter 2: Building a prediction engine

Lecture 1: What characterizes AI?

Lecture 2: Two approaches compared: a gentle introduction

Lecture 3: Human-crafted rules or machine learning?

Chapter 3: New capabilities… and limitations

Lecture 1: Three new capabilities

Lecture 2: What can't AI applications do well?

Chapter 4: From data to 'intelligence'

Lecture 1: Inputs: what is data?

Lecture 2: Outputs: predictions and automated instructions

Chapter 5: Machine learning approaches

Lecture 1: Machine learning – defined

Lecture 2: Algorithms and models

Lecture 3: Supervised and unsupervised learning

Lecture 4: Artificial neural networks and deep learning

Chapter 6: Risks and trade-offs

Lecture 1: Three drivers of new risks

Lecture 2: What could go wrong? Potential consequences

Chapter 7: How it's built

Lecture 1: Oil and data: two similar transformations

Lecture 2: Who builds AI applications?

Chapter 8: The importance of domain expertise

Lecture 1: The skills gap

Lecture 2: What do you know that data scientists might not?

Chapter 9: Bonus module: Go from observer to contributor

Lecture 1: Go from observer to contributor

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 4 votes
  • 3 stars: 38 votes
  • 4 stars: 101 votes
  • 5 stars: 102 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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