- A code-free intro to artificial intelligence, ML, & data science for professionals, marketers, managers, & executives
- Free tutorial
- Rating: 4.6 out of 54.6 (116 ratings)
- 3,899 students
- 2hr of on-demand video
- Created by Marshall Lincoln, Keyur Patel
- 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.
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
Show less
Course content
9 sections • 21 lectures • 1h 59m total lengthCollapse all sections
Demystifying AI3 lectures • 15min
- A term with many definitions04:03
- Introducing prediction engines05:04
- It’s not magic06:02
- Module 1: Quiz6 questions
Building a prediction engine3 lectures • 15min
- What characterizes AI?02:47
- Two approaches compared: a gentle introduction08:59
- Human-crafted rules or machine learning?03:29
- Module 2: Quiz6 questions
New capabilities… and limitations2 lectures • 11min
- Three new capabilities07:29
- What can’t AI applications do well?03:14
- Module 3: Quiz7 questions
From data to ‘intelligence’2 lectures • 15min
- Inputs: what is data?08:38
- Outputs: predictions and automated instructions06:06
- Module 4: Quiz6 questions
Machine learning approaches4 lectures • 22min
- Machine learning – defined02:59
- Algorithms and models04:40
- Supervised and unsupervised learning07:14
- Artificial neural networks and deep learning06:49
- Module 5: Quiz7 questions
Risks and trade-offs2 lectures • 11min
- Three drivers of new risks07:18
- What could go wrong? Potential consequences04:07
- Module 6: Quiz5 questions
How it’s built2 lectures • 11min
- Oil and data: two similar transformations07:15
- Who builds AI applications?03:23
- Module 7: Quiz5 questions
The importance of domain expertise2 lectures • 14min
- The skills gap04:47
- What do you know that data scientists might not?09:19
Bonus module: Go from observer to contributor1 lecture • 6min
- Go from observer to contributor06:06
