Resources
Curated study material to support the roadmap.
The goal is not to collect endless links. It is to point candidates toward a small set of trustworthy materials that pair well with the roadmap and question bank.
Curated links
Courses, docs, and guides that match the prep surface
Resources are grouped around applied usefulness for interviews, not around maximal breadth.
Course
Google Machine Learning Crash Course
A fast refresher for supervised learning, embeddings, evaluation, and practical ML concepts.
Course
Full Stack Deep Learning
Excellent bridge between modeling, deep learning, MLOps, and real-world AI system concerns.
Course
Hugging Face LLM Course
A practical path through transformers, tokenization, fine-tuning, and modern LLM workflows.
Guide
Made With ML
Hands-on production ML guides covering data quality, experimentation, serving, and monitoring.
Course
Stanford CS229
Theory-heavy material for linear models, probabilistic thinking, optimization, and ML fundamentals.
Guide
Chip Huyen Blog
Useful writing on ML systems, inference, latency, evaluation, and production trade-offs.
Docs
OpenAI Platform Docs
Reference for structured outputs, evaluation workflows, tool use, and production LLM patterns.
Community
Weights & Biases Reports
Good field notes on experiment tracking, model evaluation, and production workflows.
Guide
OpenAI Interview Guide
Official guidance on skills assessments, pair coding, deep technical discussions, communication, and interview expectations.
Docs
OpenAI Evaluation Best Practices
Practical guidance for creating evals, release gates, regression datasets, and model-quality checks.
Guide
AWS Machine Learning Engineer Associate Exam Guide
Role-oriented coverage of data preparation, model development, deployment, monitoring, security, and governance.
Guide
Google Cloud Professional ML Engineer Guide
Coverage guide for production ML workflows, responsible AI, model deployment, monitoring, and operationalization.
Guide
OWASP Top 10 for LLM Applications Mapping
Security risks and mitigations for LLM applications, including prompt injection, data leakage, excessive agency, and vector weaknesses.
Community
Machine Learning Interviews by alirezadir
Community question bank covering ML theory, ML coding, and ML system design prompts.
Community
Reddit ML Coding Interview Discussion
Candidate discussion highlighting practical ML coding prompts such as NumPy, PyTorch, sampling, and model-debugging tasks.
Community
Google ML System Design Mock Interview
A mock interview useful for practicing recommendation-system structure, metrics, trade-offs, and follow-up handling.