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AI/ML interview prep shaped like a feed. Every card is a full answer you can say out loud. Tap one when you want the longer version.

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Classical ML

Bias–Variance Tradeoff

When a machine learning model makes mistakes, the errors come from three main sources. First, bias happens when a model is too simple and misses the real pattern completely. Second…

4 MIN·BEGINNER

Classical ML

Overfitting vs Underfitting

Overfitting is learning the training set's noise as if it were signal — training error keeps dropping while validation error rises. Underfitting is failing to learn the signal at a…

3 MIN·BEGINNER

Classical ML

Linear Regression

Linear Regression models continuous targets as y = Xβ + ε. Ordinary Least Squares (OLS) minimizes squared residuals, yielding the closed-form solution β = (XᵀX)⁻¹Xᵀy. Under the Gau…

4 MIN·BEGINNER

Classical ML

Logistic Regression

Logistic Regression models binary probabilities using the Sigmoid activation: P(y=1|x) = σ(wᵀx + b). It maps linear log-odds to [0, 1]. Because OLS yields non-convex loss for proba…

4 MIN·BEGINNER

430

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1289

quiz questions to test yourself

areas, from statistics to system design

How it works

Open the feed and scroll
One topic fills the screen, then you swipe to the next. No course to sign up for, no chapter one. Four topics fit into five minutes in a queue.
Read the card
About 60 to 90 words, written the way you would say it in the room. If someone asks you what overfitting is, the card is what you would tell them.
Tap in when it feels shaky
The longer version answers whatever comes next. Why it is true. Where it breaks. Which numbers to quote. It ends with a paragraph you can say word for word.
Take the quiz
Reading is not knowing. The wrong answers are the ones that sound right until someone pushes, so getting it right means you understood it.