en
Feedback
Machine Learning

Machine Learning

Open in Telegram

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

Show more

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 40 149 subscribers, ranking 3 375 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 40 149 subscribers.

According to the latest data from 28 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 378 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.09%. Within the first 24 hours after publication, content typically collects 1.91% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 841 views. Within the first day, a publication typically gains 766 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 29 June, 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.

40 149
Subscribers
+724 hours
+1147 days
+37830 days
Posts Archive
πŸ“Œ Advanced Retrieval Techniques in a World of 2M Token Context Windows Part 1 πŸ—‚ Category: πŸ•’ Date: 2024-07-15 | ⏱️ Read tim
πŸ“Œ Advanced Retrieval Techniques in a World of 2M Token Context Windows Part 1 πŸ—‚ Category: πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 7 min read Exploring RAG techniques to improve retrieval

πŸ“Œ Sampling from Multivariate Distributions: From Statistical to Generative Modeling πŸ—‚ Category: πŸ•’ Date: 2024-07-15 | ⏱️ Re
πŸ“Œ Sampling from Multivariate Distributions: From Statistical to Generative Modeling πŸ—‚ Category: πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 9 min read Bridging classic statistical methods and cutting-edge generative AI models for sampling from multivariate distributions.

πŸ“Œ A Sanity Check on β€˜Emergent Properties’ in Large Language Models πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-07-15 | ⏱️ Read
πŸ“Œ A Sanity Check on β€˜Emergent Properties’ in Large Language Models πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 14 min read LLMs are often said to have ’emergent properties’. But what do we even mean by…

πŸ€–πŸ§  Ling-1T by inclusionAI: The Future of Smarter, Faster and More Efficient AI Models πŸ—“οΈ 09 Oct 2025 πŸ“š AI News & Trends A
πŸ€–πŸ§  Ling-1T by inclusionAI: The Future of Smarter, Faster and More Efficient AI Models πŸ—“οΈ 09 Oct 2025 πŸ“š AI News & Trends Artificial Intelligence is evolving at lightning speed and inclusionAI’s Ling-1T is one of the most exciting innovations leading the charge. Built on the advanced Ling 2.0 architecture, Ling-1T is a trillion-parameter model designed to combine incredible reasoning power, speed and scalability in one open-source system. Image Source : Hugging Face Unlike many AI models that ... #Ling1T #inclusionAI #ArtificialIntelligence #OpenSourceAI #LargeLanguageModels #AIArchitecture

πŸ“Œ Your 15-Minute Guide on Using Causal Inference in Business (with Placebo Tests) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07
πŸ“Œ Your 15-Minute Guide on Using Causal Inference in Business (with Placebo Tests) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 22 min read β€œCorrelation does not mean causation.” So let’s calculate causation.

πŸ“Œ VerifAI Project: Open Source Biomedical Question Answering with Verified Answers πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Dat
πŸ“Œ VerifAI Project: Open Source Biomedical Question Answering with Verified Answers πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 14 min read Experiences from building LLM-based (Mistral 7B) biomedical question-answering system with hallucination detection method

πŸ“Œ PySpark Explained: User-Defined Functions πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 10 min read Wh
πŸ“Œ PySpark Explained: User-Defined Functions πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 10 min read What are they, and how do you use them?

πŸ“Œ PyEnv & Poetry Tutorial: Ultimate Data Science Setup πŸ—‚ Category: CODING πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 8 min read Ho
πŸ“Œ PyEnv & Poetry Tutorial: Ultimate Data Science Setup πŸ—‚ Category: CODING πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 8 min read How to use PyEnv and Poetry together for your environment and package management for data…

πŸ€–πŸ§  OpenAI’s AgentKit: Transforming How Developers Build and Deploy AI Agents πŸ—“οΈ 08 Oct 2025 πŸ“š AI News & Trends OpenAI con
πŸ€–πŸ§  OpenAI’s AgentKit: Transforming How Developers Build and Deploy AI Agents πŸ—“οΈ 08 Oct 2025 πŸ“š AI News & Trends OpenAI continues to redefine the frontiers of artificial intelligence with the introduction of AgentKit a powerful all-in-one toolkit designed to simplify and accelerate how developers build and deploy AI agents. Unveiled during OpenAI’s Dev Day on October 6, 2025, AgentKit marks a transformative leap in agentic AI technology giving developers and organizations the ability to ... #OpenAI #AgentKit #AIAgents #ArtificialIntelligence #DeveloperTools #AIInnovation

πŸ“Œ Topic Modeling Open-Source Research with the OpenAlex API πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 10
πŸ“Œ Topic Modeling Open-Source Research with the OpenAlex API πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 10 min read An overview of topic modeling global research through the OpenAlex API and visualizing results

πŸ“Œ Hacking β€œCodenames” with GloVe Embeddings πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 7 m
πŸ“Œ Hacking β€œCodenames” with GloVe Embeddings πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 7 min read Using a GloVe embedding-based algorithm to achieve 100% accuracy on the popular party game β€œCodenames”

πŸ“Œ The LLM Triangle Principles to Architect Reliable AI Apps πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-16 | ⏱️ Re
πŸ“Œ The LLM Triangle Principles to Architect Reliable AI Apps πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 19 min read Software design principles for thoughtfully designing reliable, high-performing LLM applications

πŸ“Œ How to Manipulate the Total in Power BI πŸ—‚ Category: πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 8 min read In most cases, the tot
πŸ“Œ How to Manipulate the Total in Power BI πŸ—‚ Category: πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 8 min read In most cases, the total aggregates the detail rows in a visual. But what if…

πŸ€–πŸ§  Build a Large Language Model From Scratch: A Step-by-Step Guide to Understanding and Creating LLMs πŸ—“οΈ 08 Oct 2025 πŸ“š AI
πŸ€–πŸ§  Build a Large Language Model From Scratch: A Step-by-Step Guide to Understanding and Creating LLMs πŸ—“οΈ 08 Oct 2025 πŸ“š AI News & Trends In recent years, Large Language Models (LLMs) have revolutionized the world of Artificial Intelligence (AI). From ChatGPT and Claude to Llama and Mistral, these models power the conversational systems, copilots, and generative tools that dominate today’s AI landscape. However, for most developers and learners, the inner workings of these systems remain a mystery until now. ... #LargeLanguageModels #LLM #ArtificialIntelligence #DeepLearning #MachineLearning #AIGuides

πŸ“Œ The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 3) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-16 | ⏱
πŸ“Œ The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 3) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 1 min read Outliers Found: Now What? A Guide to Treatment Options

πŸ“Œ The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 3) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-16 | ⏱
πŸ“Œ The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 3) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 17 min read Outliers Found: Now What? A Guide to Treatment Options

πŸ“Œ The Math Behind Multi-Head Attention in Transformers πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-16 | ⏱️ Read ti
πŸ“Œ The Math Behind Multi-Head Attention in Transformers πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 18 min read Deep Dive into Multi-Head Attention, the secret element in Transformers and LLMs. Let’s explore its…

πŸ“Œ PyTorch Tabular: A Review πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 8 min read An overview for get
πŸ“Œ PyTorch Tabular: A Review πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 8 min read An overview for getting up and running quickly, while avoiding confusion

πŸ€–πŸ§  The Little Book of Deep Learning – A Complete Summary and Chapter-Wise Overview πŸ—“οΈ 08 Oct 2025 πŸ“š AI News & Trends In t
πŸ€–πŸ§  The Little Book of Deep Learning – A Complete Summary and Chapter-Wise Overview πŸ—“οΈ 08 Oct 2025 πŸ“š AI News & Trends In the ever-evolving world of Artificial Intelligence, deep learning continues to be the driving force behind breakthroughs in computer vision, speech recognition and natural language processing. For those seeking a clear, structured and accessible guide to understanding how deep learning really works, β€œThe Little Book of Deep Learning” by FranΓ§ois Fleuret is a gem. This ... #DeepLearning #ArtificialIntelligence #MachineLearning #NeuralNetworks #AIGuides # FrancoisFleuret

πŸ“Œ Pitching (AI) Innovation in Your Company πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 7 min re
πŸ“Œ Pitching (AI) Innovation in Your Company πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-16 | ⏱️ Read time: 7 min read Key steps to kick off an AI journey in your current job