Machine Learning interviews have 4-5 components to it.
Get ready for the hottest hiring season in Jan and Feb with this guide:👇
- the coding interview
Prepare for:
𝐓𝐡𝐞 𝐝𝐚𝐭𝐚 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬: Arrays, linked lists, stacks, queues, trees, heaps, hash tables, and graphs.
𝐓𝐡𝐞 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬: Breadth-first search (BFS), depth-first search (DFS), recursion, and sorting algorithms.
𝐁𝐢𝐠 𝐎 𝐧𝐨𝐭𝐚𝐭𝐢𝐨𝐧: trade-offs for time and space complexity
- ML fundamentals
Prepare for:
𝐂𝐨𝐫𝐞 𝐌𝐋 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬: supervised vs unsupervised, overfitting vs underfitting, bias-variance tradeoff, and regularization techniques(L1, L2) and evaluation metrics.
𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬: random forests, gradient boosting (e.g., XGBoost, LightGBM), SVMs, k-means, PCA, etc.
𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: NN, activation functions, backpropagation, CNNs, RNNs, LSTM, and transformers.
- system design (ML)
Prepare for:
𝐃𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 an end-to-end ML pipeline, from data ingestion to model deployment.
𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 & 𝐓𝐫𝐚𝐝𝐞-𝐨𝐟𝐟𝐬: latency vs throughput, how to handle large datasets, distributed training, and serving models at scale.
- behavioral interview
Prepare for:
𝐒𝐡𝐚𝐫𝐢𝐧𝐠 your impact story with the STAR (Situation, Task, Action, and Result) method.
- research/math interview (for research roles)
Prepare for:
𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚: Matrix operations, eigenvalues, eigenvectors, and singular value decomposition (SVD).
𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: Bayes’ theorem, probability distributions, hypothesis testing, and confidence intervals.
𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Gradient descent, stochastic gradient descent, and convex optimization.
𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐓𝐨𝐩𝐢𝐜𝐬: If applying for research-heavy roles, be familiar with topics like reinforcement learning, generative models (GANs, VAEs), and advanced neural architectures.
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