AI leaders need a solid grasp of the fundamentals. Before you can lead with AI — whether in business, education, or creative work — you need to understand what it actually means for a computer to “learn.”

This workshop is the first in a new series on **AI Fundamentals**, designed for absolute beginners who want to build a foundation of knowledge that supports confident, informed leadership. The sessions accompany ideas introduced in *AI Essentials for Leaders* (De Gruyter, 2025), providing a guided and highly accessible path through the core concepts every aspiring AI leader should know.

We’ll start where all AI understanding begins: the shift from **traditional programming** (where humans write explicit rules) to **machine learning** (where computers learn patterns directly from data). You’ll also explore one of the most fundamental tools in machine learning — **supervised learning**, and how it powers real-world predictions, from credit scoring to price forecasting.

No coding experience is required. Just curiosity and a desire to think like an AI leader.

## **What You’ll Learn**

You’ll develop a clear, intuitive understanding of:

* How traditional programming and machine learning differ
* Why “learning from data” represents a paradigm shift in computing
* How supervised learning enables prediction and decision-making
* What concepts like “training,” “features,” and “models” actually mean in plain English

## **Workshop Stages**

### **Stage 1: How Traditional Programming Works**

We begin with how computers have operated for decades: they follow explicit instructions written by humans. You’ll see how algorithms transform clear rules into predictable output and why this approach fails when problems become too complex or ambiguous.

**Key Learning:** Traditional programming relies on explicit human instruction.

***

### **Stage 2: How Machines Learn**

Next, we’ll look at how machine learning reverses the process. Instead of programmers defining every rule, we supply the computer with examples and correct answers so it can infer the rules itself. You’ll see, step by step, how a system builds a model from patterns hidden in data.
**Key Learning:** Machine learning extracts rules and instructions from data.

***

### **Stage 3: Learning by Example — Predicting with Regression**

We’ll walk through a conceptual case study: predicting house prices using Multiple Linear Regression. This exercise shows how a simple model can connect several factors — like size, location, and condition — to generate useful predictions.
**Key Learning:** Even basic models can reveal powerful patterns when grounded in data.

## **What You’ll Take Away**

* A foundational understanding of the shift from programming to learning
* The ability to explain supervised machine learning in plain terms
* A practical framework for evaluating where AI fits into your work or organization
* A roadmap for future workshops in the **AI Fundamentals** series

##

Generative AI for Absolute Beginners

Alfred Essa

Daisy

Brianne

Michelle Gross

Henry Harvey

Andy Jin

Chris Early

Greg Spektor

Shermaine

Jeff

Tarun Keswani

Chanel

Kelvin

Runar

Lalit Chhablani

Shamecka

Narine Bisesar

Randy

Richard J

Shepherd Runesu

AI Fundamentals: How Machines Learn

Online event

AI is transforming how software gets built — and how leaders think about innovation, productivity, and strategy. In every company and organization, intelligent systems will soon play a central role. To lead effectively, it’s not enough to use AI — you need to understand how the new paradigm of **Agentic Coding** works.

This **introductory session**, part of the *Vibe Coding for Absolute Beginners* series, offers a clear, conceptual tour of this emerging landscape. Designed for **aspiring AI leaders**, it explains how Agentic Coding differs from traditional development — and what new tools are shaping the way humans and AI collaborate to write code.

No coding experience is needed. You’ll learn the categories of tools that define modern AI development, how they fit together, and why this matters for the future of software and organizations.

***

## **What You’ll Learn**

You’ll gain an intuitive understanding of:

* The key components of the Agentic Coding environment
* How human developers and AI models now co-create software
* The role of IDEs, models, and agentic frameworks in this new workflow
* What it means to “guide” AI rather than program it

By the end, you’ll have a mental map of the agentic development stack — and a leadership-level grasp of how it changes the work of coding, collaboration, and creativity.

***

## **Workshop Stages**

### **Stage 1: The Coding Cockpit — The IDE**

Every AI developer needs a command center — a cockpit for writing, testing, and interacting with AI-generated code. We’ll start with the **Integrated Development Environment (IDE)**, focusing on **Visual Studio Code (VS Code)** as the central hub for Agentic Coding.

You’ll see how modern IDEs integrate AI directly into the workspace, providing context, suggestions, and collaboration between human and machine.

**Key Learning:** The IDE is where human intent meets AI execution — your cockpit for coding with intelligence.

***

### **Stage 2: Workflow and Specifications**

Before agents can code effectively, they need direction — a clear specification of goals, requirements, and workflow.
In this stage, we’ll go over how to **define and communicate specifications** to AI tools: what you want built, the desired behavior, and the structure of the output.

We’ll also look at **workflow practices**, including how tools like **Git** and version control systems fit into agentic collaboration — ensuring humans remain in charge of review, testing, and integration.

**Key Learning:** Clear specifications and structured workflows turn Agentic Coding from chaos into coordinated intelligence.

***

### **Stage 3: The Engines of Intelligence — The Models**

Next, we’ll look at the engines that power AI coding: large language models. We’ll explore several leading models — **Claude Sonnet**, **GPT Codex**, and **GLM** — and discuss what makes each unique in how they “understand” and generate code.

You’ll learn how these models translate natural language instructions into working code, and why selecting the right model matters.

**Key Learning:** Models are the engines of Agentic Coding — they convert ideas into executable logic.

***

**Stage 4: The Orchestrators — Agentic Coding Tools**

Finally, we’ll explore **agentic coding tools** as *orchestrators* — systems that manage, coordinate, and execute complex development workflows.
We’ll examine how platforms like **OpenAI’s Codex**, **Anthropic’s Claude Code**, and **Droid** can plan tasks, delegate work to models, and integrate results into a cohesive solution.

Rather than simply co-piloting, these agents oversee an entire process — reasoning about dependencies, sequencing steps, and adapting to feedback.
**Key Learning:** Agentic Coding tools act as orchestrators, turning AI from a helper into a system capable of managing multi-step, collaborative workflows.

***

## **What You’ll Take Away**

* A clear conceptual map of the Agentic Coding ecosystem
* Understanding of how IDEs, specifications, models, and agents work together
* Awareness of leading tools shaping the next generation of AI-driven development
* Confidence to discuss the future of software creation from a leadership perspective

Vibe Code AI for Absolute Beginners

Venu

Darryl

Paul Masson

Maciej Michael

Ryan Childress

John Armstrong

Samantha Hughes

Muktesh. C

Mohan

Jawahar Siva

Curt CurtR

Kumaran

Wendy

Akhil Venkataraju

AI Fundamentals: Introduction to Agentic Coding

AI for Absolute Beginners

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