ML Model Evaluation for Beginners: Judge Any AI Model

This course contains the use of artificial intelligence. Someone shows you a slide that says "our model is 96 percent accurate". Is that good?

Often, no. A model can score high and still find nothing useful. This course teaches you to tell the difference, in plain words, with no code and no math beyond counting.

You follow one small story from start to finish. Mina runs Harbor Bakery. Some pre-orders are never collected, so she builds a model that guesses which ones will be missed.

Then she tries to find out whether the model is any good. You watch every mistake she makes, and you learn each idea at the moment she needs it. By the end you can read a model score, spot a misleading one, and ask the right questions before anyone trusts it.

Who this course is forThis is for complete beginners. You might be a manager who hears model scores in meetings, an analyst, a student, a product owner, a tester or a founder. You do not need to write code or know statistics.

If you can count and divide, you can follow every lesson. Not for you if you want to train models in Python today, or if you already build evaluation pipelines. This course explains the thinking.

It does not teach a programming tool. What makes this course differentMost courses on this topic are built around code or heavy formulas. This one is a pure explanation course.

Every idea is drawn, counted out and attached to a story. You will see four boxes, one dial and a handful of simple numbers, and those carry the whole subject. What you will go throughModule 1, Why a Model Needs a Test: what a model and a prediction are, why accuracy fools you, why a model must be graded on data it has never seen, and how a simple baseline guess becomes your first yardstick.

Module 2, Scoring Yes or No Decisions: the confusion matrix, precision and recall, F1 and the threshold dial, and the ROC curve with AUC in plain words. Module 3, Scoring Numbers: the average miss with MAE, big misses with RMSE, what R squared tells you and what a chart of the misses tells you, and how to choose the score that matches what mistakes cost. Module 4, Can You Trust the Number?

: overfitting and underfitting, cross-validation as several exams instead of one, data leakage, and how a good average can hide a group the model fails. Module 5, In the Real World and What Next: how to judge the chatbots and AI agents everyone is discussing, a second case at a payments company, six questions that make any score judgeable, and the words and topics you meet next. The last module, named in fullModule 5, In the Real World and What Next, is the one that goes beyond the basics.

It holds a lesson on how to evaluate LLMs and AI agents, which is what everyone is discussing, a second case in a different industry with fraud checks at a payments company, a lesson with six questions to ask when someone shows you a score, and a lesson on the field's words and what to learn next. It comes with a course memo that gathers every module cheat sheet in one file, a vocabulary of 39 words and phrases you hear in meetings, job posts and interviews, and a spot-it sheet for reading job posts and answering interview questions. Every module also has a one-page memo you can print and a short optional quiz.

What is not in this courseThere is no code, no training of models, no tool tutorials and no deep math. You will not build anything. You will learn to judge.

Preview the two free lessons first.