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Machine Learning

Machine Learning

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Real Machine Learning β€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

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πŸ“ˆ Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 244 subscribers, ranking 3 343 in the Technologies & Applications category and 227 in the Syria region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 40 244 subscribers.

According to the latest data from 05 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 346 over the last 30 days and by 22 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 1.97%. Within the first 24 hours after publication, content typically collects 1.86% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 794 views. Within the first day, a publication typically gains 749 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
  • Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œReal Machine Learning β€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 06 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

40 244
Subscribers
+2224 hours
+987 days
+34630 days
Posts Archive
πŸ“Œ Spoiler Alert: The Magic of RAG Does Not Come from AI πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-17 | ⏱️ Read time: 10
πŸ“Œ Spoiler Alert: The Magic of RAG Does Not Come from AI πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-17 | ⏱️ Read time: 10 min read Why retrieval, not generation, makes RAG systems magical

πŸ“Œ How to Reduce Python Runtime for Demanding Tasks πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-17 | ⏱️ Read time: 8 min read
πŸ“Œ How to Reduce Python Runtime for Demanding Tasks πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-17 | ⏱️ Read time: 8 min read Practical techniques to accelerate heavy workloads with GPU optimization in Python

πŸ“Œ From Local to Cloud: Estimating GPU Resources for Open-Source LLMs πŸ—‚ Category: πŸ•’ Date: 2024-11-18 | ⏱️ Read time: 4 min
πŸ“Œ From Local to Cloud: Estimating GPU Resources for Open-Source LLMs πŸ—‚ Category: πŸ•’ Date: 2024-11-18 | ⏱️ Read time: 4 min read Estimating GPU memory for deploying the latest open-source LLMs

πŸ“Œ Data Validation with Pandera in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-18 | ⏱️ Read time: 10 min read Validatin
πŸ“Œ Data Validation with Pandera in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-18 | ⏱️ Read time: 10 min read Validating your Dataframes for Production ML Pipelines

πŸ“Œ Creating a frontend for your ML application with Vercel V0 πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-18 | ⏱️ Read tim
πŸ“Œ Creating a frontend for your ML application with Vercel V0 πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-18 | ⏱️ Read time: 9 min read Develop an appealing frontend application using v0 by Vercel

πŸ“Œ Navigating Networks with NetworkX: A Short Guide to Graphs in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-18 | ⏱️ Re
πŸ“Œ Navigating Networks with NetworkX: A Short Guide to Graphs in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-18 | ⏱️ Read time: 16 min read Explore NetworkX for building, analyzing, and visualizing graphs in Python. Discovering Insights in Connected Data.

πŸ“Œ Increasing Transformer Model Efficiency Through Attention Layer Optimization πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date:
πŸ“Œ Increasing Transformer Model Efficiency Through Attention Layer Optimization πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-18 | ⏱️ Read time: 16 min read How paying β€œbetter” attention can drive ML cost savings

πŸ“Œ The Metrics of Continual Learning πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-18 | ⏱️ Read time: 4 min read Thes
πŸ“Œ The Metrics of Continual Learning πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-18 | ⏱️ Read time: 4 min read These three metrics are commonly used

πŸ“Œ Building a Local Voice Assistant with LLMs and Neural Networks on Your CPU Laptop πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-
πŸ“Œ Building a Local Voice Assistant with LLMs and Neural Networks on Your CPU Laptop πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 6 min read A practical guide to run lightweight LLMs using python

πŸ“Œ Dance between dense and sparse embeddings: Enabling Hybrid Search in LangChain-Milvus πŸ—‚ Category: πŸ•’ Date: 2024-11-19 | ⏱
πŸ“Œ Dance between dense and sparse embeddings: Enabling Hybrid Search in LangChain-Milvus πŸ—‚ Category: πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 7 min read Dance Between Dense and Sparse Embeddings: Enabling Hybrid Search in LangChain-Milvus How to create and…

πŸ“Œ Multimodal Models – LLMs that can see and hear πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 10 min re
πŸ“Œ Multimodal Models – LLMs that can see and hear πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 10 min read An introduction with example Python code

πŸ“Œ The Root Cause of Why Organizations Fail With Data & AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-19 | ⏱️ Read
πŸ“Œ The Root Cause of Why Organizations Fail With Data & AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 34 min read A guide to be successful with the strategic groundwork required

πŸ“Œ NLP Illustrated, Part 1: Text Encoding πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 10 min read An illus
πŸ“Œ NLP Illustrated, Part 1: Text Encoding πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 10 min read An illustrated guide to text-to-number translation, with code

πŸ“Œ Linear programming: Integer Linear Programming with Branch and Bound πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-19 | ⏱️ Re
πŸ“Œ Linear programming: Integer Linear Programming with Branch and Bound πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 11 min read Part 4: Extending linear programming optimization to discrete decision variables

πŸ“Œ Third-Year Work Anniversary as a Data Scientist: Growth, Reflections and Acceptance πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 20
πŸ“Œ Third-Year Work Anniversary as a Data Scientist: Growth, Reflections and Acceptance πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 8 min read A letter to myself and fellow data scientists

πŸ“Œ How to Answer Business Questions with Data πŸ—‚ Category: BUSINESS πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 15 min read Data anal
πŸ“Œ How to Answer Business Questions with Data πŸ—‚ Category: BUSINESS πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 15 min read Data analysis is the key to drive business decisions through answering abstract business questions but…

πŸ“Œ Collision Risk in Hash-Based Surrogate Keys πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-11-20 | ⏱️ Read time: 14 min read
πŸ“Œ Collision Risk in Hash-Based Surrogate Keys πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-11-20 | ⏱️ Read time: 14 min read Various aspects and real-life analogies of the odds of having a hash collision when computing…

πŸ“Œ Einstein Notation: A New Lens on Transformers πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-20 | ⏱️ Read time: 9 min read
πŸ“Œ Einstein Notation: A New Lens on Transformers πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-20 | ⏱️ Read time: 9 min read Transforming the Math of the Transformer Model

πŸ“Œ Water Cooler Small Talk: Why Does the Monty Hall Problem Still Bother Us? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-20 |
πŸ“Œ Water Cooler Small Talk: Why Does the Monty Hall Problem Still Bother Us? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-20 | ⏱️ Read time: 10 min read A look at the counterintuitive mathematics of game show puzzles

πŸ“Œ LoRA Fine-Tuning On Your Apple Silicon MacBook πŸ—‚ Category: πŸ•’ Date: 2024-11-20 | ⏱️ Read time: 11 min read Let’s Go Step-
πŸ“Œ LoRA Fine-Tuning On Your Apple Silicon MacBook πŸ—‚ Category: πŸ•’ Date: 2024-11-20 | ⏱️ Read time: 11 min read Let’s Go Step-By-Step Fine-Tuning On Your MacBook