01Take-Home ProjectsOverview of take-home projects in AI hiring - how common they are, what companies use them, time expectations, and how to approach them strategically.02What Evaluators Look ForThe evaluation criteria for ML take-home projects - real rubrics, scoring systems, code quality vs modeling quality, and the production readiness signal.03Project TemplatesReusable templates for common take-home formats - classification, NLP, time series, recommendation systems, computer vision, and LLM/RAG tasks.04EDA Best PracticesExploratory data analysis that impresses - systematic approach, visualizations, statistical tests, documenting findings, and common EDA mistakes.05Model Selection StrategyChoosing the right model for a take-home - baselines, iteration strategy, hyperparameter tuning, cross-validation, and documenting your rationale.06Code Quality StandardsWriting production-quality code for take-home projects - notebook organization, function decomposition, type hints, error handling, reproducibility, testing, and clean code for data science.07The Write-UpPresenting take-home results - structuring your write-up, building compelling visualizations, writing executive summaries, creating technical appendices, delivering follow-up presentations, and handling tough questions.08Time ManagementHow to manage your time on 4-hour, 8-hour, and weekend take-home projects - time allocation, the 80/20 rule, scope management, and what to cut when you are running out of time.09Common MistakesThe most common take-home mistakes that fail candidates - data leakage, overfitting, poor evaluation, no baseline, messy notebooks, overengineering - each with examples and fixes.
