🛫 ML Roadmap 2026 — a comprehensive guide to entering ML, LLM, and MLOps

A rather insightful ML roadmap has gone viral on GitHub: within it, the author has compiled a path from a foundation in mathematics, NumPy, and Pandas to LLM, agentic RAG, fine-tuning, MLOps, and interview preparation. The repository indeed includes sections on Karpathy, MCP, RLHF, LoRA/PEFT, and system design for AI interviews.

Conveniently, this isn't just a list of random links, but rather a structured route through the topics:
▶️ Foundations and tools;
▶️ Classic ML;
▶️ LLM and agents;
▶️ Engineering and MLOps;
▶️ Interview preparation.

➡️ GitHub link:
https://github.com/loganthorneloe/ml-roadmap

tags: #ml #llm

➡ https://xn--r1a.website/CodeProgrammer

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📱 TorchCode — a PyTorch training tool for preparing for ML interviews

40 tasks for implementing operators and architectures that are actually asked in interviews. Automatic checking, hints, and reference solutions — all in the browser without installation.

If you're preparing for an ML interview, it's useful to go through at least half of them.

Link: https://github.com/duoan/TorchCode

tags: #useful #pytorch

https://xn--r1a.website/CodeProgrammer ✅

✏️ SQL for Data Science in 6 Weeks

An experienced data scientist has put together a step-by-step plan for learning SQL, organizing it into a repository on GitHub. Each week — a new topic with useful materials and practice.

The program looks like this:
▶️ Week 1 — SQL basics and data extraction;
▶️ Week 2 — grouping using GROUP BY;
▶️ Week 3 — all types of JOIN and where to apply them;
▶️ Week 4 — analyzing window functions;
▶️ Week 5 — CTE and subqueries;
▶️ Week 6 — final project to reinforce.

A great start for those who want to improve their SQL skills specifically for analytics and Data Science tasks.

➡️ Link to the course
https://github.com/andresvourakis/free-6-week-sql-roadmap-data-science

https://xn--r1a.website/DataAnalyticsX 🟡

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7️⃣ Deep Learning
8️⃣ programming Languages

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The most complete list of video courses on Computer Science on the internet.

cs-video-courses — 78K+ stars.

MIT.
Stanford University.
University of California, Berkeley.
Harvard University.
Carnegie Mellon University.
Indian Institutes of Technology.
Princeton University.
California Institute of Technology.

Everything is free. All lectures are in video format. Everything is collected in one repository.

Topics:

→ Data structures and algorithms
→ Operating systems
→ Distributed systems
→ Database systems
→ Computer networks
→ Machine learning
→ Deep learning
→ Natural language processing (NLP)
→ Computer vision
→ Computer graphics
→ Security
→ Quantum computing
→ Robotics
→ Blockchain

From beginner level (CS50) to advanced (6.824 Distributed Systems).

The curriculum is free. 🤙
https://github.com/Developer-Y/cs-video-courses

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4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

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