6 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.
Detailed AI/ML interview preparation guide: candidate side (5-stage process, 50+ technical questions with answers, ML system design, behavioral STAR, salary negotiation, Turkish company patterns), employer side (effective technical interviewing, what NOT to ask, bias-free evaluation, junior vs senior question difference), role-specific questions (Data Scientist, ML Engineer, AI Engineer, Research Scientist), Trendyol/Getir/Turkish bank interview formats, AI-assisted interview prep with GPT-5/Claude, mock interview platforms (Pramp, interviewing.io), AI cheat detection methods, live coding rules, real salary negotiation scenarios (Turkey + US remote).
AI portfolio strategy for Turkish university students (CS, EE, Industrial Engineering, Math, Statistics) from zero to graduation: 4-year year-by-year plan, 15+ recommended project types, Trendyol/Getir/Hepsiburada/Turkcell internship application process, AI opportunities at Turkish universities (AGU/Bogazici/METU/Bilkent/Hacettepe), Erasmus + European internship opportunities, Google STEP / Microsoft Explore / Meta University programs, US university masters application, GitHub + LinkedIn + personal website setup, hackathons + Teknofest + ACM ICPC, academic research + paper publication, open source contributions, Kaggle tier targets, first salary ₺40-70K (intern) → ₺60-100K (junior), Turkey-US-Europe career comparison, 10 success stories.
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.
Deep technical + career comparison of AI Engineer, ML Engineer, Data Scientist roles: historical origins (2010 Data Scientist → 2015 ML Engineer → 2023 AI Engineer), day-to-day work, tech stack (PyTorch/TF/scikit-learn vs LangChain/MCP/vector DB), Turkey salary ranges 2026 (₺55K-300K), global comparison (US $130K-500K), two main career paths (academia vs industry), 7 main differences, which role suits you, transition strategies, interview questions, seniority levels, Turkish company examples (Trendyol, Getir, Turkcell, BiTaksi), 6 Turkish specialized niches.