Machine Learning Interview Preparation Checklist (2026)

Can apply concepts like distributions, hypothesis testing, and Bayesian inference to model evaluation and uncertainty quantification.

University courses, 'Introduction to Statistical Learning'

Understands matrix operations, eigenvectors, and gradients, enabling comprehension of model internals and optimization.

Khan Academy, 3Blue1Brown YouTube series

Can implement, evaluate, and tune models like Random Forests, SVMs, and Gradient Boosting for tabular data.

Scikit-learn documentation, Kaggle micro-courses

Proficient in cross-validation, bias-variance tradeoff, and using appropriate metrics (Precision, Recall, AUC-ROC).

ML course modules on validation, 'Hands-On ML' book

Can clean data, handle missing values, encode categorical variables, and create informative features.

Pandas tutorials, feature engineering blogs

Can build and train feedforward networks from scratch, understanding activation functions, loss, and backpropagation.

Deep Learning Specialization (Coursera), PyTorch/TF tutorials

Can design CNN architectures (e.g., ResNet) for image classification, object detection, or segmentation tasks.

CS231n (Stanford), OpenCV tutorials, torchvision

Understands RNNs, LSTMs, and can implement sequence models for tasks like text classification or generation.

CS224n (Stanford), NLP with PyTorch book

Can explain and implement transformer components (attention, embeddings) and use pre-trained models from Hugging Face.

Hugging Face course, 'The Illustrated Transformer' blog

Proficient in using tools like Optuna or Ray Tune to systematically optimize model performance.

Framework documentation, research papers on optimization

Can build custom models, datasets, and training loops using PyTorch's tensor operations and autograd.

PyTorch official tutorials, 'Deep Learning with PyTorch' book

Can develop models using Keras APIs and leverage TensorFlow for production pipelines and SavedModel format.

TensorFlow certification prep, Keras documentation

Can containerize a model with Docker and deploy it as a REST API using FastAPI or Flask, or via cloud services.

MLOps Zoomcamp, cloud provider labs (AWS SageMaker, GCP AI Platform)

Can log parameters, metrics, and artifacts to track and compare experiments for reproducibility.

MLflow quickstart, Databricks community tutorials

Understands basic CUDA concepts to leverage GPU acceleration in PyTorch/TensorFlow for faster training.

NVIDIA DLI courses, framework guides on CUDA

Can fine-tune and deploy pre-trained transformer models for NLP or vision using the Transformers library.

Hugging Face documentation, model hub examples

Can implement Q-learning or policy gradient methods (e.g., with Gymnasium) for simple control tasks.

Spinning Up in Deep RL, 'Reinforcement Learning: An Introduction'

Can read recent ML papers from arXiv, understand novel architectures, and replicate core results in code.

Papers With Code, online reading groups, blog summaries

Can apply techniques like quantization, pruning, or knowledge distillation to optimize models for edge deployment.

TensorFlow Lite/PyTorch Mobile guides, research papers

Understands the principles behind generative models like GANs, VAEs, or Stable Diffusion for content creation.

Generative AI courses, Hugging Face diffusion tutorials

Can adapt large pre-trained LLMs for specific tasks using techniques like LoRA or prompt tuning.

Hugging Face PEFT library, LLM fine-tuning blogs