Machine Learning

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

Network:Machine Learning with Python Syria227 Technologies & Applications3 343...

📈 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.

📊 Audience metrics and dynamics

Since its creation on невідомо, 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.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Real Machine Learning — 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

794

Post views

~ 74924 hours

~ 98448 hours

1.97%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 244

📌 A Story of Long Tails: Why Uncertainty in Marketing Mix Modelling is Important 🗂 Category: MARKETING 🕒 Date: 2024-11-27 | ⏱️ Read time: 30 min read "Details matter. It’s worth waiting to get it right." — Steve Jobs What if the…

40 244

📌 Saving Pandas DataFrames Efficiently and Quickly – Parquet vs Feather vs ORC vs CSV 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-27 | ⏱️ Read time: 15 min read Speed, RAM, size and convenience. Which storage method is best?

40 244

📌 Use Tablib to Handle Simple Tabular Data in Python 🗂 Category: 🕒 Date: 2024-11-27 | ⏱️ Read time: 13 min read Sometimes a Shallow Abstraction is more Valuable than Performance

40 244

📌 Introducing the New Anthropic PDF Processing API 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-11-27 | ⏱️ Read time: 8 min read Anthropic Claude 3.5 now understands PDF input

40 244

📌 Roadmap to Becoming a Data Scientist, Part 1: Maths 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-27 | ⏱️ Read time: 13 min read Identifying fundamental math skills to master for aspiring Data Scientists

40 244

📌 How to Develop an Effective AI-Powered Legal Assistant 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-27 | ⏱️ Read time: 13 min read Create a machine-learning-based search into legal decisions

40 244

📌 Level Up Your Coding Skills with Python Threading 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-27 | ⏱️ Read time: 8 min read Learn how to use queues, daemon threads, and events in a Machine Learning project

40 244

📌 Effortless Data Handling: Find Variables Across Multiple Data Files with R 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-27 | ⏱️ Read time: 8 min read A practical solution with code and workflow

40 244

📌 AI Agents in Networking Industry 🗂 Category: 🕒 Date: 2024-11-27 | ⏱️ Read time: 11 min read AI Agents for deploying, configuring and monitoring Networks

40 244

📌 How to Prune LLaMA 3.2 and Similar Large Language Models 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-11-27 | ⏱️ Read time: 17 min read This article presents a structured pruning technique for state-of-the-art models, that uses a GLU architecture,…

40 244

📌 How to Transition from Engineering to Data Science 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-27 | ⏱️ Read time: 7 min read AI for engineers: experience of an engineering graduate

40 244

📌 How Can Self-Driving Cars Work Better? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-28 | ⏱️ Read time: 8 min read The far-reaching implications of Waymo’s EMMA and other end-to-end driving systems

40 244

📌 How to Select the 5 Most Relevant Documents for AI Search 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-19 | ⏱️ Read time: 10 min read Improve the document retrieval step of your RAG pipeline

40 244

📌 An Interactive Guide to 4 Fundamental Computer Vision Tasks Using Transformers 🗂 Category: COMPUTER VISION 🕒 Date: 2025-09-19 | ⏱️ Read time: 14 min read An overview of 4 fundamental computer vision tasks – image classification, image segmentation, image captioning…

40 244

📌 LLMs.txt Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-11-28 | ⏱️ Read time: 6 min read Your guide to the web’s new LLM-ready content standard

40 244

Я получил свои первые TON за 5 минут — и ничем не рисковал! «Думал, что это очередной фейк… но TON реально пришли на счет» Хочешь также? Узнай, как получить до 10 TON без вложений — уже сегодня. #ad InsideAds .

40 244

📌 The Economics of Artificial Intelligence – what does automation mean for workers? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-11-25 | ⏱️ Read time: 40 min read Despite tremendous progress in AI, the economic implications of AI remain inadequately understood, with unsatisfactory…

40 244

📌 RAGOps Guide: Building and Scaling Retrieval Augmented Generation Systems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-11-26 | ⏱️ Read time: 28 min read The Architecture, Operational Layers, and Best Practices for Effective RAG Implementation

40 244

📌 Every Step of the Machine Learning Life Cycle Simply Explained 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-26 | ⏱️ Read time: 18 min read A comprehensive guide to the ML life cycle with examples in Python

40 244

“Sadece 8 PUMP ile işlem başlatabileceğini kimse bana inanmamıştı!” Her referans 2 PUMP veriyor, görevleri tamamla ve sırrı burada keşfet — kimse fark etmeden airdropları topla! #ad InsideAds