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Machine Learning & AI

Machine learning interview preparation in depth: how models learn, regression, classification, evaluation and production concerns, with mathematics and Python code.

3 topics21 questions & lessons

01 INTERVIEW SET

ML Interview Part 1: Foundations, Linear & Logistic Regression

4 sections · Open → 02 INTERVIEW SET

ML Interview Part 2: Learning Types, Trees, Forests, KNN, SVM, Naive Bayes & Ensembles

7 sections · Open → 03 INTERVIEW SET

ML Interview Part 3: Gradient Descent, Bias–Variance, Validation, Clustering, PCA, XGBoost, Neural Networks & Metrics

10 sections · Open →

Topics

Optional study guide

See the big picture · Machine Learning & AI

From historical data to a deployed model

Start with the diagram, compare the concepts, then apply them to the lesson.

DataCollect and clean labelled examples.
TrainFit parameters by minimising a loss.
EvaluateMeasure on data the model has not seen.
DeployServe predictions and monitor drift.

At a glance
Problem	Typical algorithm
Predict a number (price, demand)	Linear regression, gradient boosting
Predict a class (default, spam)	Logistic regression, random forest
Group similar items	K-means, DBSCAN
Recommend jobs or products	Similarity / ranking models

Watch for this: High accuracy on imbalanced data can hide a useless model; check precision, recall and calibration.