NVIDIA, Wharton, Google, & 125 ML models
Tech CxO, AI Quality pioneer, former CDO
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Most business professionals still wait weeks or months for data teams to turn their questions into working machine learning models. In that time, opportunities slip away and decisions get made on gut feel instead of evidence.
At the same time, modern LLMs like Claude can now guide non‑technical professionals through the core steps of building simple, explainable models in minutes instead of months, if you know how to use them well and safely.
In this 3‑hour workshop, you'll learn a structured process for turning a real business question into a small, defensible ML model with an LLM as your copilot.
You'll walk through:
- clarifying the decision
- shaping a prediction or classification task
- preparing tabular data
- asking an LLM to design and explain a model
- deciding how to use it responsibly inside your organization
- proving its worth to stakeholders
You'll walk away with a reusable workflow, prompt patterns, and a clear story you can share with stakeholders about what these models can and cannot do.
Go from “I’m not a data scientist” to using an LLM to design, build, and explain a simple machine learning model in your own domain.
You'll walk through exercises and a template to map questions to outcomes.
You'll uncover exactly what you need in a CSV file or dataframe.
You'll learn everything Dr. Nicole Radziwill created her own book on model techiques.
You'll understand how to convert cold logic into a compelling story for stakeholders.
The key criteria for determining how much you can address yourself will be presented in a convenient checklist.
You'll understand the "what," the "why," and the "how" to master model outputs.
- Introductions - Case study: a before/after example of a business user leveraging an LLM to build a model in under 15 minutes - Review the big idea: LLM as ML copilot
-ML problems to choose: binary classification, simple regression, simple ranking - The “translation step”: business question → target variable → features → rows - When to use GenAI vs other tools
- Defining “good enough” for simple models - Common issues: missing values, outliers, leakage, sketchy labels - Using an LLM to profile a dataset and document data assumptions
- Optimal models for non‑technical teams: logistic regression, decision trees, very small ensembles - How to ask an LLM for a model design
- Why “explainability is power” for non‑technical audiences -Basic evaluation loop: train → test → interpret → refine
- Online vs offline deployment: what they mean for risk, monitoring, and change management - When you need data science or engineering teams - A simple checklist before using LLM‑assisted models
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Top AI pioneer helping executives from Google to NVIDIA to Yale University.

Tech CxO, AI Quality pioneer, former CDO & Chief Data Scientist
Key executives who need timely results without all the jargon
Managers looking to advance to the next level
Managers who want to prepare themselves for the next level by enhancing their profile
Live sessions
Learn directly from T. Scott Clendaniel & Dr. Nicole Radziwill in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Checklists to keep your team on track
You'll never lose project details in the rush to achieve.
Maven Guarantee
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Reimbursement
Get your company to pay
Everything L&D needs: email template, receipts, and certificate of completion.
Get reimbursedTeam discount
Learn with your teammates
Save 20%+ when 2 or more teammates enroll in the same cohort.
Save 20%+ with a teamPrivate cohort
Run a cohort for your org
A dedicated cohort with a custom schedule and curriculum, tailored to your team.
Book a private cohort$375
USD
1–4pm EDT