Amplifying Human Ingenuity with Intelligent Technology
Requirements
No tools are required, just basic knowledge and experience on business administration
Description
Peter Maynard, Director Program Management at Microsoft, explores what AI really is, why and how it will transform every business in every industry. Peter also uncovers how Microsoft technology is at the forefront of this transformation and show some scenarios, both present and future with respect to how this is helping business embrace digital transformation.
The purpose of the course is to highlight how underlying Digital Transformation in a number of enterprises is simply an algorithm. This algorithm will determine the success of how that company will leverage its data in the future and if it will ultimately survive. Moving on from that as background, there will be then explored the types of steps that a company can take to win in the algorithm wars and things that they should be conscious of. In the course, there will be presented a range of examples of companies that are winning in Digital Transformation through AI.
Who this course is for:
Professionals who want to explore what AI really is, why and how it will transform every business in every industry.
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
