Multi-Label Text Classification with Scikit-LLM 📝

In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training. 🚀

Topics we will cover include:

What multi-label classification is and why it matters for nuanced text analysis. 📊
How to set up and configure scikit-LLM with a free, open-source LLM from Groq for zero-shot inference. ⚙️
How to load a real-world dataset and run multi-label sentiment predictions using a familiar scikit-learn-style workflow. 📈

Read: https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/ 🔗

#ScikitLLM #TextClassification #LLM #MachineLearning #ZeroShot #DataScience

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
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👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

10 GitHub repositories that are worth checking out for an AI engineer 🤖

1. Hands-On AI Engineering 🛠️

A collection of AI applications and agent systems with practical use cases of LLM.

👉 https://github.com/Sumanth077/Hands-On-AI-Engineering

2. Hands-On Large Language Models 📘

Full code from the book Hands-On Large Language Models: from basics to fine-tuning.

👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

3. AI Agents for Beginners 🎓

A free course from Microsoft with 11 lessons on creating AI agents.

👉 https://github.com/microsoft/ai-agents-for-beginners

4. GenAI Agents 🤖

A large collection of tutorials and implementations of agent systems.

👉 https://github.com/NirDiamant/GenAI_Agents

5. Made With ML 🚀

About the development, deployment, and support of production-ready ML systems.

👉 https://github.com/GokuMohandas/Made-With-ML

6. Learn Harness Engineering ⚙️

A practical course on Harness Engineering for AI agents.

👉 https://github.com/walkinglabs/learn-harness-engineering

7. AutoResearch 🔬

Autonomous cycles of ML experiments from Andrej Karpathy.

👉 https://github.com/karpathy/autoresearch

8. Designing Machine Learning Systems 📚

Notes and materials from Chip Huyen's book.

👉 https://github.com/chiphuyen/dmls-book

9. Awesome LLM Inference ⚡

A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.

👉 https://github.com/xlite-dev/Awesome-LLM-Inference

10. LLM Course 🗺️

A practical course on LLM with a roadmap and Colab notebooks.

👉 https://github.com/mlabonne/llm-course

#AI #MachineLearning #LLM #DataScience #Tech #GitHub

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

Anthropic, together with Frontend Masters, have launched a free course on Claude Code. 🚀

And this is not a superficial overview, but a thorough analysis of the tool for those who want to really improve their vibe coding and work with AI agents. 🤖

The course is led by Lydia Hallie from Anthropic. 👩‍💻

Inside:

• basics of Claude Code
• skills
• hooks
• sub-agents
• MCP
• plugins
• Agent SDK
• advanced work scenarios

A good entry point for those who want not just to "ask AI to write code", but to build a proper workflow around Claude Code. 🛠️

Link:
http://frontendmasters.com/courses/claude-code

#Anthropic #FrontendMasters #ClaudeCode #AIAgents #Coding #LLM

📚 "Natural Language Processing and Large Language Models" is a new open-access book from Springer, written by Chengqing Zong, Yang Zhao, and Yanjun Ma.

It's almost 400 pages long and provides an introduction to modern natural language processing and large language models.

Inside, you'll find information on: neural networks, distributed representations, language models, Transformers, BERT, GPT, tokenization, sentiment analysis, information extraction, text summarization, natural language understanding, machine translation, question answering, and RLHF.

In my opinion, this is a good reference guide for those who want to understand these topics without a very high barrier to entry. I would recommend it. ✨

https://link.springer.com/book/10.1007/978-981-92-0682-7

#NLP #LLM #ArtificialIntelligence #MachineLearning #DataScience #TechBooks

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📚 This is probably one of the best technical books on how large language models are trained at scale:

> GPU memory and profiling
> Breaking down computations into blocks, kernel fusion, and FlashAttention
> Data parallelism, tensor parallelism, pipeline parallelism, and context parallelism

I've already read the free online version, but I still had to buy a physical copy for my library. 📖

You can also read it for free on Hugging Face:

https://huggingface.co/spaces/nanotron/ultrascale-playbook

#LLM #AI #MachineLearning #TechBooks #DataScience #Coding

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