Algorithms You Must Master

In this section, we get a clear roadmap of the core algorithms we need to master for ML algorithm coding interviews. These are foundational models that balance simplicity with depth—perfect for testing both theory and implementation skills under time pressure.

• For clustering, we should be able to implement k-means from scratch, including centroid initialization, assignment, recomputation, and convergence checks. We should also understand evaluation metrics like inertia and silhouette score.
• For linear models, we must code linear and logistic regression with gradient descent, using proper loss functions and optionally adding L1/L2 regularization.
• For tree-based methods, we should implement a basic decision tree, and if time allows, understand how random forests build on it using bagging and bootstrapping.
• In k-nearest neighbors, we focus on distance calculations (Euclidean vs. cosine) and how to retrieve the top-k neighbors for a query point.
• Lastly, for neural networks, we should know how to build a simple feedforward network with one or two hidden layers and train it using backpropagation, including activation functions like ReLU and sigmoid, along with their gradients.

By preparing these specific algorithms, we’ll be equipped to handle most ML algorithm coding interviews with confidence and precision.

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