# LeetCode vs Kaggle vs Real Project 2026: Which First for AI Engineers? Deep Turkish Decision Guide

> Source: https://sukruyusufkaya.com/en/blog/leetcode-kaggle-real-project-karsilastirma
> Updated: 2026-05-13T21:00:42.827Z
> Type: blog
> Category: yapay-zeka
**TLDR:** 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.

<tldr data-summary='["3 learning paths serve different purposes: LeetCode (algorithm focus, Big Tech interview), Kaggle (competition + ML algorithms + Notebooks tier), Real Project (end-to-end, GitHub portfolio, production experience). For 2026 Turkey, Real Project + Kaggle hybrid is optimal.","Priority changes by TARGET COMPANY: Big Tech (Google, Meta) LeetCode dominant (60% time). Turkish companies (Trendyol, Getir) Real Project + Kaggle (70%). European mixed. Research role: paper + academic.","For JUNIOR candidates PRIORITY ORDER: 1) Real Project (5+ GitHub repos, 1+ deployed), 2) Kaggle Expert tier, 3) LeetCode 100 medium. Senior: Real Project >> other two.","Real Project concrete benefits: GitHub stars, deployment URL, blog writing, product/SaaS launch, user feedback. Provides hard skill AND business sense. STRONGEST evidence for JOB SEARCH.","Kaggle concrete benefits: Deep ML algorithm understanding, ensemble techniques, real-world data (noise + bias), Notebooks tier contributes to job finding.","LeetCode concrete benefits: Big Tech gateway (Google, Meta, Amazon, OpenAI), strengthens algorithm fundamentals. Useful but not vital for Turkish companies.","12-month optimal hybrid distribution: Real Project 50%, Kaggle 30%, LeetCode 20%. For Big Tech target: 30%/30%/40% (LeetCode increases). For Turkish tech unicorn: 60%/30%/10%."]' data-one-line="3 learning paths different purpose — Real Project portfolio + production, Kaggle ML + medal, LeetCode Big Tech interview. Turkey 2026 optimal hybrid: 50/30/20 Real/Kaggle/LeetCode."></tldr>

## 1. Three Paths Different Purposes

- LeetCode: Big Tech interview prep
- Kaggle: ML algorithm depth + competition
- Real Project: portfolio + production experience

## 2. Target Company → Mix

- Turkish tech: 50% Real Project + 30% Kaggle + 20% LeetCode
- Big Tech: 30% Real Project + 30% Kaggle + 40% LeetCode
- Solo SaaS founder: 90% Real Project

## 3. LeetCode Details

3000+ problems, Easy/Medium/Hard, Premium $35/mo. Target: 100-150 medium for Turkish tech, 200-300 for Big Tech.

## 4. Kaggle Details

5-tier system (Novice → Grandmaster), 4 categories. Target: Expert tier for junior, Master+ for senior.

## 5. Real Project Details

End-to-end deployed product: README + demo URL + tests + CI/CD + blog post + LinkedIn announcement.

## 6. Job Rejection Distribution Turkey 2026

45% Real Project weak, 25% LeetCode/algorithm weak, 15% ML/AI knowledge, 10% behavioral, 5% salary expectations.

## 7. Conclusion

12-month optimal hybrid: 50/30/20 Real/Kaggle/LeetCode for Turkish tech. Adjust based on target company. Quality > quantity always.