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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
Want any LLM to answer from your own documents?
Most RAG setups quietly give weak, vague answers, and the model is almost never the real problem. Three small fixes decide whether it works, and the exact tools to use in 2026 are very specific.
Full, concrete guide in one post.
🔖 Cheat sheets on Transformers and LLMs from Stanford
We found an excellent set of materials from the Stanford CME-295 course on large language models.
They cover tokenization, the self-attention mechanism, prompting, fine-tuning, LLM-as-a-judge, RAG, AI agents, and reasoning models.
⛓️ Download here
https://github.com/afshinea/stanford-cme-295-transformers-large-language-models
🎓 A Free AI Course for Beginners by Microsoft
For those just getting into artificial intelligence, Microsoft offers a free course.
It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.
The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.
Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.
➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md
What's your AI level right now?
❤️ — Advanced user
🔥 — Almost zero
#AICourse #Microsoft #DeepLearning #TensorFlow #PyTorch #MachineLearning
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Free certificates
I often see people say that it's impossible to enter the IT field without expensive courses.
However, there's a huge amount of high-quality materials available for free:
📚 Computer Science
https://github.com/ossu/computer-science
📚 Data Structures & Algorithms
https://github.com/jwasham/coding-interview-university
📚 System Design
https://github.com/donnemartin/system-design-primer
📚 Web Development
https://github.com/TheOdinProject/curriculum
📚 Frontend / Backend / DevOps / Cloud
https://github.com/kamranahmedse/developer-roadmap
📚 Data Engineering
https://github.com/DataTalksClub/data-engineering-zoomcamp
📚 Machine Learning & AI
https://github.com/microsoft/ML-For-Beginners
📚 MLOps
https://github.com/DataTalksClub/mlops-zoomcamp
📚 Cybersecurity
https://github.com/OWASP/CheatSheetSeries
📚 Linux
https://github.com/trimstray/the-book-of-secret-knowledge
📚 Free Programming Books
https://github.com/EbookFoundation/free-programming-books
If you have internet and a bit of free time, you can learn computer science, algorithms, system design, DevOps, clouds, security, and machine learning for free.
The problem now isn't a lack of information. The problem is regularly opening these repositories and actually working on them.
#FreeLearning #ITCareer #CodingResources #TechEducation #OpenSource #DevCommunity
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Agentic Design Patterns - a free 421-page document from a senior Google engineer. 📄✨
It's rare to find materials of such a large volume where the author doesn't try to sell a course after every chapter. 💡
Inside:
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If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. 😅
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. 🤖
Instead of endless Google searches, everything is organized into categories:
• fundamentals of machine learning
• neural networks and modern architectures
• tasks and application areas
• datasets
• libraries and tools
• fairness and AI ethics
• production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. 📝
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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5 Fun Papers That Explain LLMs Clearly 📚✨
Want to understand LLMs better? Start with these five foundational papers that explain how they work. 🤖
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. 📄 This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. 🔬 In this article, we will go through five papers that explain how LLMs work. So, let's get started. 🚀
More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly
#LLM #AI #MachineLearning #DeepLearning #DataScience #Tech
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A new collection of free courses has been added:
🔗 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. 📚
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:
• Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
• A table with lectures, descriptions, videos, notes, and authors
• Links to the original lectures and accompanying notes
• WIP markers for incomplete materials
• Instructions for contributors on adding and improving notes
The idea was appreciated. 👍
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. 🗺️
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
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On Reddit, someone collected all the resources they used to prepare for interviews on algorithms, system design, and machine coding. In the end, they made it to a Google interview.
1. Algorithms and Patterns
Before grinding on problems, it's worth understanding the patterns.
• All LeetCode Articles on Coding Patterns Summarized
https://leetcode.com/discuss/interview-question/5366542/all-leetcode-articles-on-coding-patterns-summarized-in-one-page
• Solved All Two Pointers Problems in 100 Days
https://leetcode.com/discuss/study-guide/1688903/Solved-all-two-pointers-problems-in-100-days
• Tree Question Pattern 2023 — Tree Study Guide
https://leetcode.com/discuss/study-guide/2879240/tree-question-pattern-2023-tree-study-guide
• Important and Useful Links from All Over LeetCode
https://leetcode.com/discuss/general-discussion/665604/Important-and-Useful-links-from-all-over-the-LeetCode
• Coding Interview Preparation Problems for Beginners
https://leetcode.com/discuss/interview-question/448284/Coding-Interview-preparation-problems-for-beginners
2. Preparation for Companies
• Google, Meta, Apple, Amazon Senior SDE Preparation
https://prachub.com/?sort=hot&company=Meta%2CGoogle%2CTikTok%2CAmazon
• A Study Guide for Passing the Google Interview
https://prachub.com/interview-guide
The author also made a small tracker for preparation:
• company-specific questions
• Todo / Solved / Revision statuses
• automatic repetition scheduling
• AI assistant with hints instead of ready-made solutions
https://prachub.com/questions
3. System Design (HLD)
Instead of random articles — structured collections:
• Arch 25 — the most common systems and patterns
• Arch 50 — infrastructure, data, and fault tolerance
• Arch 75 — more complex scenarios and company-specific specialization
• Arch All — a full bank of 103 HLD tasks
• Core Concepts — 33 breakdowns of distributed systems
4. Machine Coding / LLD
Many underestimate this part until their first interview failure.
• MaCo 30 — the most common tasks
• MaCo 60 — an extended collection
• MaCo All — a full set of 103 tasks
• Design Patterns — 31 design patterns
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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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🧩 Local AI is no longer just a toy project.
In 2026, you can run a practical AI stack on a laptop: small LLMs, local embeddings, RAG, Jupyter/IDE integration, and no token bill.
This post breaks down what is actually usable right now: Qwen, Gemma, Llama, Ollama, Chroma/LanceDB, local RAG, Jupyter AI, hardware limits, and where local video still hurts.
Read the local stack
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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☕️ Python Tutorial — a detailed course for beginners!
A comprehensive Python training course that systematically covers the basics of the language: variables, conditions, loops, functions, lists, dictionaries, OOP, etc. The material is accompanied by code examples and clear explanations, making it suitable even for those who are just starting their journey in programming.
📌 I'll leave a link: beginnersbook.com
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Kaggle and Google are launching a free 5-day intensive course on VibeCoding. 🚀
Over five days, they will explain how agent-based coding works, how to configure skills, memory, and context, and how to make AI assistants not only useful but also safe. 🤖 The program includes daily tasks, live streams, discussions, and practical projects. 💻
After completion, participants will receive an official certificate that can be added to their portfolio. 📜
Start date: June 15th. 📅
https://www.kaggle.com/competitions/5-day-ai-agents-intensive-vibecoding-course-with-google 🔗
If you've long wanted to properly understand agent-based coding, this is a great opportunity to dive in. 🌊
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Don't want to read about AI Engineering, but actually want to build something? 🛠️
We've released a collection of 50+ practical tutorials on AI Engineering as open source. 📚
Inside, you'll find step-by-step projects on:
• AI agents and multi-agent systems 🤖
• RAG (Agentic, Vision, and Local) 🔍
• MCP agents ⚙️
• OCR applications 📄
• Voice AI agents 🎙️
• and much more ✨
Everything is free, with source code and ready-made examples. 🥩
https://github.com/Sumanth077/Hands-On-AI-Engineering
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GRU → remembers efficiently
GAN → creates realistic content
RNN → understands sequences
LSTM → remembers long context
Introduction to Critical Infrastructure Protection (ICIP) 🛡️
Your CIP Cybersecurity Career Path 🚀
Learn about Cybersecurity in Critical Infrastructure Protection (CIP) 🔐
Explore our learning paths and philosophy 💡
Get certified - Free 🎓
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Join our livestream with Marina Wyss, Senior Applied Scientist at Twitch, as we discuss how to break into AI Engineering in 2026.
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Why should you join?
Many people interested in AI Engineering are asking the same questions:
❓ Where do I start?
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🧠 Should I focus on ML, LLMs, RAG, or AI agents?
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If you’re interested in AI Engineering but unsure how to approach it, this livestream is for you.
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Data Science Interview Questions
💡 Here is your curated list for Data Science interviews!
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🔖 The Big Book on Fine-Tuning LLMs
A free 115-page book dedicated to the retraining of large language models. 📚
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. 🚀
#LLM #FineTuning #AI #MachineLearning #DataScience #Tech
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