Subjects

Decision Trees

Learn how decision trees partition feature space using impurity measures, how recursive binary splitting works, which hyperparameters control overfitting, and how feature importance is calculated — the foundation for understanding gradient boosting models like LightGBM and XGBoost.

Model Serving & Deployment

Learn to design the serving side of an ML system: batch vs online vs streaming vs hybrid inference, latency budgets with numbers, model server patterns, compression, canary and shadow rollouts, feature-store consistency, autoscaling, and safe fallbacks.

Clustering & Dimensionality Reduction

Learn k-means, hierarchical clustering, DBSCAN and Gaussian mixtures, how to choose k and evaluate clusters without labels, and how PCA, t-SNE and UMAP compress high-dimensional data — with a worked PCA example and interview traps.

Bias–Variance Trade-off & Cross-Validation

Master the bias-variance decomposition, learning and validation curves, k-fold, stratified, group and time-series cross-validation, nested CV for honest tuning, and the data-leakage traps that make validation scores lie.

Regularization: L1, L2 and Elastic Net

Learn how Ridge (L2), Lasso (L1) and Elastic Net penalties shrink or zero out coefficients, why lasso does feature selection and ridge does not, how to pick lambda, and how the same idea shows up in logistic regression, neural nets and gradient boosting.

Model Training & Experimentation at Scale

Learn how to answer the training half of an ML system design interview: baselines, model family choice, temporal offline evaluation, tuning budgets, experiment tracking, distributed training, GPU cost estimation, embedding tables, retraining cadence, and off-policy evaluation.

Model Evaluation Metrics

Master the confusion matrix and its derived metrics, ROC vs precision-recall curves, log loss, calibration and ECE, cost-based threshold selection, regression and ranking metrics like NDCG, multiclass averaging, and how to tell real metric gains from noise.

ML Monitoring, Drift & Retraining

Learn why deployed models decay, how to monitor them in layers from system health to business KPIs, compute PSI and other drift statistics with worked numbers, handle delayed labels, and design retraining triggers with safe validation gates and rollback.

ML Data Pipelines & Feature Stores

Design the data side of an ML system: logging for train/serve parity, labelling strategies, point-in-time joins, negative sampling, batch vs streaming features, feature stores, training–serving skew, data validation, lineage and a worked notification-click pipeline.

Multimodal LLMs and Vision

Learn how multimodal LLMs actually process images: patch tokenization, vision-encoder pretraining, and how the model distinguishes between multiple objects in a scene through attention rather than bounding-box regression. Covers the practical gap between vision-language models and classical object detectors, common failure modes (counting, fine-grained discrimination, spatial relations), and prompting techniques (referring expressions, crops, set-of-mark) that make multi-object questions reliable in production.

Feature Engineering

Learn the feature engineering toolkit data scientists are tested on: scaling and transforms per model family, categorical encodings including leakage-safe target encoding, missing-value strategies, cyclical time features, point-in-time aggregations, feature selection, and sklearn pipelines that prevent training/serving skew.