Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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Forwarded from Data Analytics
The ultimate guide to fine tuning.pdf
15.2 MB
πŸ”– The Big Book on Fine-Tuning LLMs

A free 115-page book dedicated to the retraining of large language models. πŸ“š

It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. πŸš€

#LLM #FineTuning #AI #MachineLearning #DataScience #Tech

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πŸš€ Level up your AI & Data Science skills with HelloEncyclo β€” a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
βœ… 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
πŸ”‘ Use code: PRESALE-BOOK-WAVE-2GFG
πŸ‘‰ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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Data Science Interview Questions.pdf
1.4 MB
Data Science Interview Questions

πŸ’‘ Here is your curated list for Data Science interviews!

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

#DataScience #AI #MachineLearning #LLM #TechJobs #InterviewPrep
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A new collection of free courses has been added:

πŸ”— https://github.com/dair-ai/ML-Course-Notes

Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. πŸ“š

Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. 🧠

What's inside:

β€’ Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
β€’ A table with lectures, descriptions, videos, notes, and authors
β€’ Links to the original lectures and accompanying notes
β€’ WIP markers for incomplete materials
β€’ Instructions for contributors on adding and improving notes

The idea was appreciated. πŸ‘

Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. πŸ—ΊοΈ

#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource

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πŸš€ Level up your AI & Data Science skills with HelloEncyclo β€” a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
βœ… 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
πŸ”‘ Use code: PRESALE-BOOK-WAVE-2GFG
πŸ‘‰ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❀8
5 Fun Papers That Explain LLMs Clearly πŸ“šβœ¨

Want to understand LLMs better? Start with these five foundational papers that explain how they work. πŸ€–

Large language models (LLMs) can feel complicated at first. There are transformers, attention layers, scaling laws, pretraining, instruction tuning, human feedback, retrieval, and many other ideas around them. 🧠 But the best way to understand large language models is not to start with a huge textbook. A better way is to read a few important papers that each explain one major part of the system. πŸ“„ This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. πŸ”¬ In this article, we will go through five papers that explain how LLMs work. So, let's get started. πŸš€

More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly

#LLM #AI #MachineLearning #DeepLearning #DataScience #Tech

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πŸš€ Level up your AI & Data Science skills with HelloEncyclo β€” a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
βœ… 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
πŸ”‘ Use code: PRESALE-BOOK-WAVE-2GFG
πŸ‘‰ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❀4
Forwarded from Machine Learning
If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. πŸ˜…

Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. πŸ€–

Instead of endless Google searches, everything is organized into categories:

β€’ fundamentals of machine learning
β€’ neural networks and modern architectures
β€’ tasks and application areas
β€’ datasets
β€’ libraries and tools
β€’ fairness and AI ethics
β€’ production ML and MLOps

Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. πŸ“

I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. ⚠️

https://github.com/ZhiningLiu1998/awesome-machine-learning-resources

#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources

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πŸš€ Level up your AI & Data Science skills with HelloEncyclo β€” a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
βœ… 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
πŸ”‘ Use code: PRESALE-BOOK-WAVE-2GFG
πŸ‘‰ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❀7
Forwarded from Machine Learning
Multi-Label Text Classification with Scikit-LLM πŸ“

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. πŸš€

Topics we will cover include:

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

Read: https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/ πŸ”—

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

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πŸš€ Level up your AI & Data Science skills with HelloEncyclo β€” a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
βœ… 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
πŸ”‘ Use code: PRESALE-BOOK-WAVE-2GFG
πŸ‘‰ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❀3
10 GitHub repositories that are worth checking out for an AI engineer πŸ€–

1. Hands-On AI Engineering πŸ› οΈ

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

πŸ‘‰ https://github.com/Sumanth077/Hands-On-AI-Engineering

2. Hands-On Large Language Models πŸ“˜

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

πŸ‘‰ https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

3. AI Agents for Beginners πŸŽ“

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

πŸ‘‰ https://github.com/microsoft/ai-agents-for-beginners

4. GenAI Agents πŸ€–

A large collection of tutorials and implementations of agent systems.

πŸ‘‰ https://github.com/NirDiamant/GenAI_Agents

5. Made With ML πŸš€

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

πŸ‘‰ https://github.com/GokuMohandas/Made-With-ML

6. Learn Harness Engineering βš™οΈ

A practical course on Harness Engineering for AI agents.

πŸ‘‰ https://github.com/walkinglabs/learn-harness-engineering

7. AutoResearch πŸ”¬

Autonomous cycles of ML experiments from Andrej Karpathy.

πŸ‘‰ https://github.com/karpathy/autoresearch

8. Designing Machine Learning Systems πŸ“š

Notes and materials from Chip Huyen's book.

πŸ‘‰ https://github.com/chiphuyen/dmls-book

9. Awesome LLM Inference ⚑

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

πŸ‘‰ https://github.com/xlite-dev/Awesome-LLM-Inference

10. LLM Course πŸ—ΊοΈ

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

πŸ‘‰ https://github.com/mlabonne/llm-course

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

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πŸš€ Level up your AI & Data Science skills with HelloEncyclo β€” a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
βœ… 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
πŸ”‘ Use code: PRESALE-BOOK-WAVE-2GFG
πŸ‘‰ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
πŸ‘2❀1