Prepare for ML interviews covering fundamentals, algorithms, deep learning, and real-world ML system design problems like search ranking, recommendations, and ad prediction.
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Core machine learning concepts every interview candidate must know — learning paradigms, bias-variance tradeoff, evaluation metrics, and model selection.
Master the art of feature engineering — handling missing data, scaling, encoding, feature selection, and dimensionality reduction with PCA.
Deep dive into the classic ML algorithms — linear models, trees, SVMs, KNN, Naive Bayes, and ensemble methods with sklearn implementations.
Neural network fundamentals through transformers — activation functions, backpropagation, CNNs, RNNs, and the attention mechanism.
Design a search ranking system from scratch — problem formulation, features, training data, model architecture, and evaluation.
Design a recommendation system end-to-end — problem formulation, collaborative and content-based filtering, feature engineering, model architecture, and evaluation.
Design an ad prediction system — CTR prediction, feature engineering, model evolution from logistic regression to deep models, training pipelines, and online serving.
Design a social media feed ranking system — multi-objective optimization, feature engineering, multi-task learning, and evaluation of engagement, quality, and diversity.