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 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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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
πŸ“Œ How to Optimize Your AI Coding Agent Context

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2026-01-06 | ⏱️ Read time: 7 min read

Make your coding agents more efficient

#DataScience #AI #Python
πŸ“Œ GliNER2: Extracting Structured Information from Text

πŸ—‚ Category: NATURAL LANGUAGE PROCESSING

πŸ•’ Date: 2026-01-06 | ⏱️ Read time: 11 min read

From unstructured text to structured Knowledge Graphs

#DataScience #AI #Python
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πŸ“Œ Feature Detection, Part 3: Harris Corner Detection

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-01-05 | ⏱️ Read time: 7 min read

Finding the most informative points in images

#DataScience #AI #Python
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πŸ“Œ Measuring What Matters with NeMo Agent Toolkit

πŸ—‚ Category: LLM APPLICATIONS

πŸ•’ Date: 2026-01-06 | ⏱️ Read time: 13 min read

A practical guide to observability, evaluations, and model comparisons

#DataScience #AI #Python
πŸ“Œ The Best Data Scientists Are Always Learning

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-06 | ⏱️ Read time: 10 min read

Part 2: Avoiding burnout, learning strategies and the superpower of solitude

#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
πŸ“Œ HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ Date: 2026-01-07 | ⏱️ Read time: 18 min read

How approximate vector search silently degrades Recallβ€”and what to do about It

#DataScience #AI #Python