Machine Learning
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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.

Admin: @HusseinSheikho || @Hussein_Sheikho
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πŸ“Œ EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-01 | ⏱️ Read time: 13 min read

How to build, score, and interpret RFM segments step by step

#DataScience #AI #Python
Harvard has made its textbook on ML systems publicly available. It's extremely practical: not just about how to train models, but how to build production systems around them - what really matters.

The topics there are really top-notch:

> Building autograd, optimizers, attention, and mini-PyTorch from scratch to understand how the framework is structured internally. (This is really awesome)
> Basic things about DL: batches, computational accuracy, model architectures, and training
> Optimizing ML performance, hardware acceleration, benchmarking, and efficiency

So this isn't just an introductory course on ML, but a complete cycle from start to practical application. You can already read the book and view the code for free. For 2025, this is one of the strongest textbooks to have been released, so it's best not to miss out.

The repository is here, with a link to the book inside πŸ‘

πŸ‘‰ @codeprogrammer
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πŸ“Œ Deep Reinforcement Learning: The Actor-Critic Method

πŸ—‚ Category: REINFORCEMENT LEARNING

πŸ•’ Date: 2026-01-01 | ⏱️ Read time: 19 min read

Robot friends collaborate to learn to fly a drone

#DataScience #AI #Python
Cheat sheet for Python for Data Science: covers basic Python syntax (variables, data types, operations, strings), working with lists, NumPy arrays, indexing and slicing, main methods and functions, as well as importing libraries for data analysis

https://t.me/DataScienceM
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πŸ“Œ Drift Detection in Robust Machine Learning Systems

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-01-02 | ⏱️ Read time: 18 min read

A prerequisite for long-term success of machine learning systems

#DataScience #AI #Python
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πŸ“Œ Off-Beat Careers That Are the Future Of Data

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-02 | ⏱️ Read time: 8 min read

The unconventional career paths you need to explore

#DataScience #AI #Python
πŸ“Œ The Real Challenge in Data Storytelling: Getting Buy-In for Simplicity

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-02 | ⏱️ Read time: 7 min read

What happens when your clear dashboard meets stakeholders who want everything on one screen

#DataScience #AI #Python
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All assignments for the #Stanford The Modern Software Developer course are now available online.

This is the first full-fledged university course that covers how code-generative #LLMs are changing every stage of the development lifecycle. The assignments are designed to take you from a beginner to a confident expert in using AI to boost productivity in development.

Enjoy your studies! ✌️
https://github.com/mihail911/modern-software-dev-assignments

https://t.me/CodeProgrammer
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πŸ“Œ Optimizing Data Transfer in AI/ML Workloads

πŸ—‚ Category: DEEP LEARNING

πŸ•’ Date: 2026-01-03 | ⏱️ Read time: 16 min read

A deep dive on data transfer bottlenecks, their identification, and their resolution with the help…

#DataScience #AI #Python
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πŸ“Œ How to Keep MCPs Useful in Agentic Pipelines

πŸ—‚ Category: AGENTIC AI

πŸ•’ Date: 2026-01-03 | ⏱️ Read time: 10 min read

Check the tools your LLM uses before replacing it with just a more powerful model

#DataScience #AI #Python
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πŸ”– 40 NumPy methods that cover 95% of tasks

A convenient cheat sheet for those who work with data analysis and ML.

Here are collected the main functions for:
▢️ Creating and modifying arrays;
▢️ Mathematical operations;
▢️ Working with matrices and vectors;
▢️ Sorting and searching for values.


Save it for yourself β€” it will come in handy when working with NumPy.

tags: #NumPy #Python

➑ @DataScienceM
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πŸ“Œ Prompt Engineering vs RAG for Editing Resumes

πŸ—‚ Category: LLM APPLICATIONS

πŸ•’ Date: 2026-01-04 | ⏱️ Read time: 12 min read

Running a code-free comparison in Azure

#DataScience #AI #Python
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πŸ“Œ How to Filter for Dates, Including or Excluding Future Dates, in Semantic Models

πŸ—‚ Category: DATA ANALYSIS

πŸ•’ Date: 2026-01-04 | ⏱️ Read time: 5 min read

It is common to have either planning data or the previous year’s data displayed beyond…

#DataScience #AI #Python
nature papers: 1400$

Q1 and  Q2 papers    900$

Q3 and Q4 papers   500$

Doctoral thesis (complete)    700$

M.S thesis         300$

paper simulation   200$

Contact me
https://t.me/m/-nTmpj5vYzNk
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OnSpace Mobile App builder: Build AI Apps in minutes

Visit website: https://www.onspace.ai/?via=tg_datas
Or Download app:https://onspace.onelink.me/za8S/h1jb6sb9?c=datas

With OnSpace, you can build website or AI Mobile Apps by chatting with AI, and publish to PlayStore or AppStore.

What will you get:
βœ”οΈ Create app or website by chatting with AI;
βœ”οΈ Integrate with Any top AI power just by giving order (like Sora2, Nanobanan Pro & Gemini 3 Pro);
βœ”οΈ Download APK,AAB file, publish to AppStore.
βœ”οΈ Add payments and monetize like in-app-purchase and Stripe.
βœ”οΈ Functional login & signup.
βœ”οΈ Database + dashboard in minutes.
βœ”οΈ Full tutorial on YouTube and within 1 day customer service
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πŸš€ Master Data Science & Programming!

Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!


πŸ”° Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer

πŸ”– Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM

🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4

🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ

πŸ’Ύ Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1

πŸ§‘β€πŸŽ“ Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC

πŸ˜€ ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT

πŸ’¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9

🐍 Python Arab| Ψ¨Ψ§ΩŠΨ«ΩˆΩ† عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab

πŸ–Š Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksβ€”insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN

πŸ“Ί Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV

πŸ“ˆ Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX

🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.me/Python53

⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY

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Admin: @HusseinSheikho
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πŸ“Œ Stop Blaming the Data: A Better Way to Handle Covariance Shift

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-05 | ⏱️ Read time: 9 min read

Instead of using shift as an excuse for poor performance, use Inverse Probability Weighting to…

#DataScience #AI #Python
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πŸ“Œ YOLOv1 Loss Function Walkthrough: Regression for All

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2026-01-05 | ⏱️ Read time: 26 min read

An explanation of how YOLOv1 measures the correctness of its object detection and classification predictions

#DataScience #AI #Python