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 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
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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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

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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
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…

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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

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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

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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

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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

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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
πŸ“Œ I Evaluated Half a Million Credit Records with Federated Learning. Here’s What I Found

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-07 | ⏱️ Read time: 12 min read

Why privacy breaks fairness at small scaleβ€”and how collaboration fixes both without sharing a single…

#DataScience #AI #Python
πŸ“Œ Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ Date: 2026-01-07 | ⏱️ Read time: 21 min read

Human-guided AI collaboration

#DataScience #AI #Python
πŸ“Œ Why Supply Chain is the Best Domain for Data Scientists in 2026 (And How to Learn It)

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-07 | ⏱️ Read time: 13 min read

My take after 10 years in Supply Chain on why this can be an excellent…

#DataScience #AI #Python
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