A code-free intro to artificial intelligence, ML, & data science for professionals, marketers, managers, & executives
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:
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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 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
Course content
9 sections • 21 lectures • 1h 59m total length
