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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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Machine Learning with Python

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Machine Learning with Python

🚀 Looking for a portfolio-ready NLP project?

I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.

You’ll see how far classical NLP can go with:

📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking

It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:

🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/

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Machine Learning with Python

🎓 Free Courses on Claude and AI From Anthropic

Anthropic has expanded its educational platform, Anthropic Academy. It now offers more than 20 free courses on using Claude and AI-powered tools.

No programming experience is required. There are programs for students, educators, nonprofits, and small businesses. The courses cover how to give models clear instructions, evaluate their responses, and automate everyday tasks.

Developers can also take dedicated courses on the Claude API, Claude Code, Model Context Protocol, Agent Skills, and cloud integrations.

All courses are free, and you don't need a Claude account to enroll. All materials and videos are in English.

📌 Participants receive official certificates after completing many of the programs.

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Planning to take one?

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Machine Learning with Python

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Machine Learning with Python

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Machine Learning with Python

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Machine Learning with Python

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Machine Learning with Python

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Machine Learning with Python

A Chinese developer has released an open-source replacement for NumPy that performs calculations on GPUs. It's called CuPy 🚀. In many cases, it's enough to replace a single line:

import cupy as cp

The same code can run on CUDA up to 100 times faster ⚡️.

What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.

The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy

#Python #GPU #NumPy #CuPy #AI #DeepLearning

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Machine Learning with Python

A guide to Loop Engineering has been released — a new approach to working with AI agents

The repository loop-engineering has been published, offering a paradigm shift: instead of manually prompting AI agents, the developer designs a cycle that does this automatically. 🔄🤖

The author notes that most people still use Claude Code, Codex, Cursor, and Grok as a regular chat: prompt → wait → copy → correct → prompt again. Loop Engineering proposes to stop being a "nanny" for the agent and instead build a system where agents work, check, correct, and escalate on their own. 🛠️⚙️

The repository includes ready-made cycles for daily triage, PR, CI, dependencies, changelog, and issues. It includes CLI for creating cycles, evaluating tokens, auditing the repository, and safely running agents via GitHub Actions. 📋✅

"Prompt engineering was about how to write better prompts. Loop engineering is about creating a system where agents continue to work without your supervision at every step," the description says. 🚀🧠

The repository is available on GitHub.

Repository: https://github.com/cobusgreyling/loop-engineering

#LoopEngineering #AI #Agents #GitHub #DevOps #Automation

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Machine Learning with Python

There are hundreds of AI channels on YouTube. Here's why we made another one.

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AI vs ML vs Deep Learning vs GenAI ... But Done Right!
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Machine Learning with Python

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

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Machine Learning with Python

Guide to scalable system design and interviews 🚀📊

https://github.com/karanpratapsingh/system-design

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Machine Learning with Python

Learn AI for free directly from top companies. 🚀

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9 - DeepLearning.AI:
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10 - Hugging Face:
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💬 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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Machine Learning with Python

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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Machine Learning with Python

Reinforcement Learning Methods and Tutorials 🧠📚

In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.

Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow 🚀

Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. 📖✨

#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience

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Machine Learning with Python

My new hobby: searching for beautiful projects on GitHub and being inspired by their design. 🚀✨

https://www.gitsearchai.com/ 🔗

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Machine Learning with Python

🔖 One of the most useful books on Agentic AI

This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents.

It's an excellent option to get a holistic picture and understand which topics deserve deeper study.

⛓️ Link to the book
https://arxiv.org/abs/2606.24937

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Machine Learning with Python

Understanding Datasets 😉

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Machine Learning with Python

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Machine Learning with Python

Transformers become more understandable when you can "poke" the model directly. 🧠✨

Transformer Explainer is an interactive visualization tool for studying how text-generating transformer-based models, such as GPT, work. 🔍

It helps connect the architecture with real behavior by running a live GPT-2 directly in the browser, allowing you to enter your own text and showing how the internal components work together to predict the next tokens. 🔄📝

Key features: 🌟

- Live GPT-2 in the browser - experiment without setting up a separate model server 💻
- Your own text - try your own prompts and see how the model processes them ✍️
- Internal components - observe the operations working inside the transformer 🔧
- Focus on predicting the next token - link each visual step to the model's predictions 🎯
- Local development - clone the repository, install dependencies, and run via npm for in-depth study ⚙️

It's open-source (MIT license). 📜

https://github.com/poloclub/transformer-explainer

#AI #MachineLearning #GPT #DataScience #TechTools #OpenSource

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Machine Learning with Python

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Machine Learning with Python

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Machine Learning with Python

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Master the Google Cloud Generative AI Leader Certification with 600+ Realistic Practice Questions, Detailed Explanation…

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Machine Learning with Python

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Machine Learning with Python

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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Machine Learning with Python

Curated list of distributed systems papers and books 📚📖

https://github.com/theanalyst/awesome-distributed-systems

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Machine Learning with Python

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]

The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯

It's more reliable to gather everything into one expression:

s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]

One mask, one point of truth. ✅

It's easier to debug. Fewer surprises when the code grows. 🚀

#Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging

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Machine Learning with Python

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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🚀 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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Machine Learning with Python

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.

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