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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
There are hundreds of AI channels on YouTube. Here's why we made another one.
Most AI content does one of two things: it stays so surface-level it teaches you nothing, or it goes so deep you need a PhD to follow along.
We built Guidely for everyone in between.
→ We start with absolute beginners in mind
→ Then take you deeper, until the details actually click
→ Every guide is reviewed by experienced AI engineers
→ We don't make more content. We make better content.
Whether you build, design, or market products, our goal is simple: leave you thinking "I've never seen it broken down this well."
Two good places to start 👇
→ AI vs ML vs Deep Learning vs GenAI ... But Done Right!
The terms everyone uses. The distinctions are almost never explained clearly. We fix that: youtu.be/72yyLA2wRWc
→ How to Break into AI Engineering in 2026
A senior applied scientist shares what actually matters: youtu.be/42vE7Ij4kdU
If AI has ever felt overwhelming or noisy, this channel is for you. If the content resonates with you, please don’t forget to like and subscribe.
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
Guide to scalable system design and interviews 🚀📊
https://github.com/karanpratapsingh/system-design
#SystemDesign #Scalability #TechInterviews #SoftwareEngineering #DevCommunity #Coding
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Learn AI for free directly from top companies. 🚀
1 - Anthropic:
anthropic.skilljar.com
2 - Google:
grow.google/ai
3 - Meta:
ai.meta.com/resources/
4 - NVIDIA:
developer.nvidia.com/cuda
5 - Microsoft:
learn.microsoft.com/en-us/training/
6 - OpenAI:
academy.openai.com
7 - IBM:
skillsbuild.org
8 - AWS:
skillbuilder.aws
9 - DeepLearning.AI:
deeplearning.ai
10 - Hugging Face:
huggingface.co/learn
💬 Comment "Learning" if you find this helpful.
🔄 Repost so others can take help.
🔖 Must bookmark for future reference.
#AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll
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The guide Path to Senior Engineer Handbook has gathered resources for developers who want to advance to the level of Senior Engineer. 🚀
Inside: 📚
More than 50 newsletters on professional growth, system design, leadership, and web development. 📈
A selection of books on communication, technical writing, and building working relationships. 🤝
Selected YouTube channels, podcasts, and professional communities. 🎧
Courses, scientific articles, and educational platforms for a deeper study of topics. 🎓
A good starting point for those who want to improve not only their technical skills, but also their architectural thinking, communication, and leadership competencies. 💡
Link: https://github.com/jordan-cutler/path-to-senior-engineer-handbook?utm_source=opensourceprojects.dev&ref=opensourceprojects.dev
#SeniorEngineer #CareerGrowth #SoftwareEngineering #TechLeadership #SystemDesign #DevCommunity
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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
#Python #Tutorial #Coding #Programming #LearnToCode #TechSkills
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✅ 13 courses live + 40+ coming soon
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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. 🌊
#Kaggle #Google #VibeCoding #AIAgents #Coding #AI #TechNews #FreeCourse
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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
#AIEngineering #MachineLearning #OpenSource #AIProjects #TechTutorials #BuildWithAI
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GRU → remembers efficiently
GAN → creates realistic content
RNN → understands sequences
LSTM → remembers long context
500 AI/ML/Computer Vision/NLP projects with code 🚀
This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP 🧠
All examples come with code, so you can not just read them, but immediately analyze and run them ⚙️
➡️ Link to GitHub:
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
#AI #MachineLearning #DeepLearning #ComputerVision #NLP #DataScience
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Curated list of distributed systems papers and books 📚📖
https://github.com/theanalyst/awesome-distributed-systems
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My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼
The chain looks neat, but breaks on real data and easily gives unexpected results:
s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]
s = pd.Series([10, 15, 20, 25, 30])
mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]
Classical machine learning equations and diagrams cheat sheet 📊
https://github.com/soulmachine/machine-learning-cheat-sheet
#MachineLearning #ML #DataScience #CheatSheet #AI #DeepLearning
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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:
⬩ agent architectures
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#AgenticAI #LLM #GoogleEngineering #MultiAgentSystems #AIDevelopment #TechDocs
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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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