AI foundations for business professionals
- None whatsoever. This course is designed to help complete beginners in the field of AI make the transition to informed participants in the workplace.
Full course outline:
Module 1: Demystifying AI
A term with any definitions
An objective and a field
Excitement and disappointment
Introducing prediction engines
Introducing machine learning
Don't expect 'intelligence' (It's not magic)
Module 2: Building a prediction engine
What characterizes AI? Inputs, model, outputs
Two approaches compared: a gentle introduction
Building a jacket prediction engine
Human-crafted rules or machine learning?
Module 3: New capabilities... and limitations
Expanding the number of tasks that can be automated
New insights --> more informed decisions
Personalization: when predictions are granular... and cheap
What can't AI applications do well?
Module 4: From data to 'intelligence
What is data?
Machine learning unlocks new insights from more types of data
What do AI applications do?
Predictions and automated instructions
When is a machine 'decision' appropriate?
Module 5: Machine learning approaches
Machine learning basics
What's an algorithm?
Traditional vs machine learning algorithms
What's a machine learning model?
Machine learning approaches
Artificial neural networks and deep learning
Module 6: Risks and trade-offs
Beware the hype
Three drivers of new risks
What could go wrong? Potential consequences
Module 7: How it's built
It's all about data
Oil and data: two similar transformations
The anatomy of an AI project
The data scientist's mission
Module 8: The importance of domain expertise
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
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:
- Board members
- Line of business managers
- Other business professionals who want to engage with AI projects
- Students and anyone contemplating a future in data science
- 04:03A term with many definitions
- 05:04Introducing prediction engines
- 06:02It's not magic
- 6 questionsModule 1: Quiz
As a practitioner of data science, Marshall has led business transformation projects and hired, managed and worked alongside data science teams. He works with international blue-chip organizations and has authored incisive research into the responsible and effective use of AI.
Marshall is Co-author and Chief Data Scientist at FluentinAI[dot]com. Fluent in AI is an online data science course designed to help non-technical business stakeholders find a common language with the technical teams they partner with, to deliver effective AI and ML solutions at their organizations.
Marshall is Co-founder of the Lucid Analytics Project - an Impact research consultancy which looks at cross-industry applications of AI, combining experience and interviews with practitioners and risk managers, data scientists, technology providers, consultants, regulators, and academics to produce long-form research.
He is also the founder and CEO of BHN Ventures (a management consulting firm focused on the intersection of applied data science, customer data platforms, and digital marketing)
Marshall's prior experience has equipped him with a deep and comprehensive understanding of the end-to-end business intelligence function (especially at SaaS companies): process, tools, methodologies, technologies, objectives
As an independent journalist and consultant, Keyur has written features and analysis for a number of leading international publishers. He has also produced independent research for dozens of organizations, including blue chip companies, academic bodies, and not-for-profits.
He is a research associate at the Centre for the Study of Financial Innovation - and has authored multiple reports for the Centre, which have been widely covered in the international media. He was formerly Marjorie Deane Fellow at the Financial Times and is a graduate in economics from University College London.