[Free] Ai Foundations For Business Professionals
A code-free intro to artificial intelligence, ML, & data science for professionals, marketers, managers, & executives – Free Course
What you’ll learn
- This course provides students with a broad introduction to AI. Students will be equipped with 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 machine learning – 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 and where 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
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A term with any definitions
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An objective and a field
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Excitement and disappointment
Lecture 2:
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Introducing prediction engines
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Introducing machine learning
Lecture 3
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Prediction engines
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Don’t expect ‘intelligence’ (It’s not magic)
Module 2: Building a prediction engine
Lecture 4:
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What characterizes AI? Inputs, model, outputs
Lecture 5:
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Two approaches compared: a gentle introduction
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Building a jacket prediction engine
Lecture 6:
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Human-crafted rules or machine learning?
Module 3: New capabilities… and limitations
Lecture 7
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Expanding the number of tasks that can be automated
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New insights –> more informed decisions
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Personalization: when predictions are granular… and cheap
Lecture 8:
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What can’t AI applications do well?
Module 4: From data to ‘intelligence
Lecture 9
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What is data?
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Structured data
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Machine learning unlocks new insights from more types of data
Lecture 10
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What do AI applications do?
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Predictions and automated instructions
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When is a machine ‘decision’ appropriate?
Module 5: Machine learning approaches
Lecture 11
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Three definitions
Machine learning basics
Lecture 12
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What’s an algorithm?
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Traditional vs machine learning algorithms
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What’s a machine learning model?
Lecture 13
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Machine learning approaches
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Supervised learning
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Unsupervised learning
Lecture 14
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Artificial neural networks and deep learning
Module 6: Risks and trade-offs
Lecture 15:
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Beware the hype
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Three drivers of new risks
Lecture 16
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What could go wrong? Potential consequences
Module 7: How it’s built
Lecture 17
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It’s all about data
Oil and data: two similar transformations
Lecture 18
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The anatomy of an AI project
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The data scientist’s mission
Module 8: The importance of domain expertise
Lecture 19:
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The skills gap
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A talent gap and a knowledge gap
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Marrying technical sills and domain expertise
Lecture 20: What do you know that data scientists might not?
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Applying your skills to AI projects
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What might you know that data scientists’ not?
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How can you leverage your expertise?
Module 9: Bonus module: Go from observer to contributor
Lecture 21
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Go from observer to contributor
