𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝗳𝗼𝗿 𝗝𝗼𝗯 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀.

In DS or AI/ML interviews, you need to be able to explain models, debug them live, and design AI/ML systems from scratch. If you can’t demonstrate this during an interview, expect to hear, “We’ll get back to you.”

The attached person's name is Chip Huyen. Hopefully you know her; if not, then I can't help you here. She is probably one of the finest authors in the field of AI/ML.

She designed proper documentation/a book for common ML interview questions.

Target Audiences: ML engineer, a platform engineer, a research scientist, or you want to do ML but don’t yet know the differences among those titles.Check the comment section for links and repos.

📌 link:
https://huyenchip.com/ml-interviews-book/

#JobInterview #MachineLearning #AI #DataScience #MLEngineer #AIInterview #TechCareers #DeepLearning #AICommunity #MLSystems #CareerGrowth #AIJobs #ChipHuyen #InterviewPrep #DataScienceCommunit


https://t.iss.one/CodeProgrammer 🌟

👨🏻‍💻 This Python library helps you extract usable data for language models from complex files like tables, images, charts, or multi-page documents.

📝 The idea of Agentic Document Extraction is that unlike common methods like OCR that only read text, it can also understand the structure and relationships between different parts of the document. For example, it understands which title belongs to which table or image.


✅ Works with PDFs, images, and website links.

☑️ Can chunk and process very large documents (up to 1000 pages) by itself.

✔️ Outputs both JSON and Markdown formats.

☑️ Even specifies the exact location of each section on the page.

✔️ Supports parallel and batch processing.

pip install agentic-doc

┌ 🥵 Agentic Document Extraction
├ 🌎 Website
└ 🐱 GitHub Repos

🌐 #DataScience #DataScience
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https://t.iss.one/CodeProgrammer

📺 12 comprehensive playlists to master
⬅️ machine learning, deep learning, and GenAI!


👨🏻‍💻 Each playlist is designed to be simple and understandable for beginners, and then gradually dive deeper into the topics.


😉 Machine Learning Basics (39 videos)

😉 Python for ML (9 videos)

😉 Optimization for ML (5 videos)

😉 Machine Learning with Practical Exercises (37 videos)

😉 Building Decision Trees from Scratch (13 videos)

😉 Building Neural Networks from Scratch (35 videos)

😉 Graph Neural Networks (6 videos)

😉 Computer Vision from Scratch (19 videos)

😉 Building LLM from Scratch (43 videos)

😉 Reasoning in LLMs from Scratch (22 videos)

😉 Building DeepSeek from Scratch (29 videos)

😉 Machine Learning in Production Environment (6 videos)



🌐 #Data_Science #DataScience
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https://t.iss.one/CodeProgrammer ❤️