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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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 🤖
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• 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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🚀 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
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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
⬩ multi-agent systems
⬩ memory and context management
⬩ orchestration and task planning
⬩ tools, MCP, and integrations
⬩ production cases and code examples

#AgenticAI #LLM #GoogleEngineering #MultiAgentSystems #AIDevelopment #TechDocs

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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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🚀 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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⏰ Mid-Year Deal Ends Soon – Don't Miss Out!

☕️ 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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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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✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
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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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✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO