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Courses>Other IT & Software>AI foundations for business professionals
IT & SoftwareAI foundations for business professionals
Price:Free
Length:2 hours
Content type:video
level:all levels
Updated:19 February 2024
Published:21 August 2022
Similar courses
Opportunities
Courses>Other IT & Software>AI foundations for business professionals
AI foundations for business professionals
4.7 (2.3k)
2 hours
2261 students
What you will learn
1This 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.
2The main differences between building a prediction engine using human-crafted rules and machine learning - and why this difference is central to AI.
3Three key capabilities that AI makes possible, why they matter, and what AI applications cannot yet do.
4The 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'.
5The main principles behind the machine learning and deep learning approaches that power the current wave of AI applications.
6Artificial neural networks and deep learning: the reality behind the hype.
7Three main drivers of risks which are characteristic of AI, why they arise, and their potential consequences in a workplace environment.
8An overview of how AI applications are built - and who builds them (with the help of extended analogy).
9Why one of the biggest problems the AI industry faces today - a pronounced skills gap - represents an opportunity for students.
10How to use their own knowledge, skills and expertise to provide valuable contributions to AI projects.
11Students will learn how to build upon the foundations they learned upon in this course, to make the move from informed observer to valuable contributor.
Target audiences
1This 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:
2Executives
3Board members
4Line of business managers
5Analysts
6Marketers
7Other business professionals who want to engage with AI projects
8Students and anyone contemplating a future in data science
Requirements
1None whatsoever. This course is designed to help complete beginners in the field of AI make the transition to informed participants in the workplace.
FAQ
You can view and review the lecture materials indefinitely, like an on-demand channel.
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!
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

Similar courses
Opportunities
Make the most out of your online education
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All rights reserved
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Careertail
Courses>Other IT & Software>AI foundations for business professionals
IT & SoftwareAI foundations for business professionals
Price:Free
Length:2 hours
Content type:video
level:all levels
Updated:19 February 2024
Published:21 August 2022
Similar courses
Opportunities
Courses>Other IT & Software>AI foundations for business professionals
AI foundations for business professionals
4.7 (2.3k)
2 hours
2261 students
What you will learn
1This 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.
2The main differences between building a prediction engine using human-crafted rules and machine learning - and why this difference is central to AI.
3Three key capabilities that AI makes possible, why they matter, and what AI applications cannot yet do.
4The 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'.
5The main principles behind the machine learning and deep learning approaches that power the current wave of AI applications.
6Artificial neural networks and deep learning: the reality behind the hype.
7Three main drivers of risks which are characteristic of AI, why they arise, and their potential consequences in a workplace environment.
8An overview of how AI applications are built - and who builds them (with the help of extended analogy).
9Why one of the biggest problems the AI industry faces today - a pronounced skills gap - represents an opportunity for students.
10How to use their own knowledge, skills and expertise to provide valuable contributions to AI projects.
11Students will learn how to build upon the foundations they learned upon in this course, to make the move from informed observer to valuable contributor.
Target audiences
1This 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:
2Executives
3Board members
4Line of business managers
5Analysts
6Marketers
7Other business professionals who want to engage with AI projects
8Students and anyone contemplating a future in data science
Requirements
1None whatsoever. This course is designed to help complete beginners in the field of AI make the transition to informed participants in the workplace.
FAQ
You can view and review the lecture materials indefinitely, like an on-demand channel.
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!
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

Similar courses
Opportunities
Make the most out of your online education
Careertail
Copyright © 2021 Careertail.
All rights reserved
Quick Links
Get StartedLog InAbout UsCourses
Company
BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions
Stay up to date
Trustpilot