3 posts
Deep comparison of 3 main learning paths for AI/ML engineering candidates + students: LeetCode (algorithm focus, Big Tech interview), Kaggle (competition + ML algorithm + Notebooks tier), Real Project (end-to-end, GitHub portfolio, production experience). Each path strengths, time investment, job-finding contribution, position-priority matching, Turkish company vs US Big Tech vs European differences, hybrid strategy recommendations, Junior vs Senior focus difference, 12-month recommended mix, time-investment ROI calculation, 8 success stories, common mistakes, post-interview feedback distribution.
Comprehensive Turkish guide for learning data science with Kaggle from zero to Master: platform structure (Notebooks, Competitions, Datasets, Models, Discussions), 5 progression tiers (Novice → Contributor → Expert → Master → Grandmaster), per-tier requirements + process, 20+ free Kaggle Learn courses, 6-month plan from first competition to first medal, ensemble + stacking + blending techniques, GPU/TPU notebook strategies, tabular vs CV vs NLP competition differences, Turkish Kaggle masters success stories, team formation tactics, code competitions, Notebooks tier separate path, dataset/discussion medal strategy, optimizing Kaggle profile for job hunting, 10 practical tips.
Detailed 12-month roadmap to become an AI engineer from zero: Month 1-2 Python + math foundation, Month 3-4 classic ML, Month 5-6 deep learning + PyTorch, Month 7-8 LLM + RAG + agentic, Month 9-10 MLOps + production, Month 11-12 specialized + job search. Each month with specific courses (Coursera, fast.ai, DeepLearning.AI), books, milestone projects, Turkish resources (BTK Akademi, Coursera Turkish subtitles), daily study plan, portfolio requirements (5-10 GitHub projects), Kaggle strategy, certifications, job application tactics. SMB/freelance/abroad options.