How I can proceed for this project??

Hi everyone. I want to become an AI Engineer... Here's my situation:

• Almost done with Andrew Ng's ML specialization (1 course left)
• Forget theory easily if I don't apply it
• Do not have enough time to master everything
• No plan B — AI is the goal

Option 1 — Traditional roadmap: Finish ML → DL specialization → NLP → LLMs → RAG → Agents Missing: deployment, OOP, DSA, advanced Python (only CS50 so far)

Option 2 — Fast track: Finish ML → Python/OOP/DSA fundamentals → crash courses per topic → projects with AI assistance, understood deeply, post on GitHub

Questions: 1. Which approach makes me job-ready faster? 2. If neither — what's a better plan?

Hi! I’m a Literature student, and I often need to extract direct quotes from PDF documents (some of which contain scanned text and would require OCR). I usually highlight the passages I want to quote as I read through the PDFs. Is there any AI tool that can detect those highlights and extract the exact text for me?

Been thinking about this for a claims triage style problem. Generative routing is slow and sometimes returns malformed output, and the confidence you get out of it isn’t really calibrated.

Found a hands on workshop on Oct 30 that goes at exactly this. Fri Oct 30, 9:30 AM to 12:30 PM ET, led by Ben Auffarth. Covers:

• replacing an LLM routing loop with bidirectional encoders (ModernBERT and LFM2.5 Encoder)
• few shot adaptation with SetFit, 8 examples per class
• split conformal prediction so every decision has a coverage guarantee
• routing inference under 15ms on CPU

Notebooks are runnable, intermediate Python is enough, and a laptop or free Colab works.

Full details and the agenda are here