AI foundations for business professionals

Rating 4.4 out of 5 (46 ratings in Udemy)

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 …

Rating 4.4 out of 5 (46 ratings in Udemy)

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.

Description

Full course outline:

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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 AIapplications 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 AIproject
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 AIprojects
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

Duration 2 Hours 58 Minutes

Free

Self paced

All Levels

English (US)

2290

Rating 4.4 out of 5 (46 ratings in Udemy)

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