Most ML education is either too academic or too notebook-driven. ML Fluency is different: two separate tracks, one for decision makers and one for practitioners, built around realistic business scenarios instead of disconnected toy examples.
For PMs, analysts, operators, and business leaders who need better ML judgment.
For engineers and data scientists who want stronger implementation and production intuition.
Most people do not need the same kind of ML education. Decision makers need judgment. Practitioners need production intuition.
This is not one generic ML course pretending to work for everyone.
Built for PMs, analysts, operators, founders, and business leaders working with ML teams.
Built for engineers and data scientists who want more than definitions and polished notebooks.
You are not just consuming content. You are reading, deciding, building, debugging, and applying.
Short, high-signal concept explanation without bloated lectures.
Practice judgment in realistic ML product situations.
Apply the concept immediately in Colab with minimal friction.
Inspect a broken setup and figure out what went wrong.
Keep the core ideas, patterns, and red flags for later use.
The full program is larger, but this gives a feel for the shape and emphasis of each track.
Framing, evaluation, tradeoffs, and practical GenAI literacy.
Rules vs ML, framing templates, baselines, and cost of being wrong.
Understand what a churn model is actually doing and how it gets used.
Why feature quality, data shape, and leakage matter more than hype.
Business metrics, calibration, thresholding, and decision quality.
Prompting, RAG, guardrails, and how to evaluate GenAI features sensibly.
Implementation depth, debugging skill, and production intuition.
See the full pipeline and the most common failure points.
Catch inflated metrics before they become production failures.
Choose metrics that match the real decision instead of the easiest chart.
Understand the workhorses behind modern AI systems.
Move from notebook thinking to systems that survive production.
Decision Makers is for PMs, analysts, operators, founders, and business leaders. Practitioners is for engineers and data scientists who actively build ML systems.
No for Decision Makers. Yes, you should already be comfortable with Python and pandas for Practitioners.
A browser and a Google account. The hands-on work runs in Colab, so there is no local environment setup requirement.
Yes. It is fully async, with no live sessions, no cohort deadlines, and no subscription model.
The easiest way to know whether this fits your role is to open one module and go through the full learning loop yourself.
If you want access to the complete course, email me at 189investmentai@gmail.com for details.