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 364...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 150 subscribers, ranking 3 364 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 40 150 subscribers.

According to the latest data from 27 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 412 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 1.96%. Within the first 24 hours after publication, content typically collects 1.89% reactions from the total number of subscribers.
Post reach: On average, each post receives 785 views. Within the first day, a publication typically gains 760 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
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 28 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.

40 150

Subscribers

+524 hours

+1067 days

+41230 days

785

Post views

~ 76024 hours

~ 1 06948 hours

1.96%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 151

📌 Acquire Customers with Ecommerce Data Science 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-05 | ⏱️ Read time: 7 min read Data informed strategies help ecommerce businesses overcome advertising challenges

40 151

📌 Cross-validation with XGBoost – Enhancing Customer Churn Classification with Tidymodels 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-06 | ⏱️ Read time: 6 min read Step-by-step guide to implementing cross-validation, feature engineering, and model evaluation with XGBoost in Tidymodels

40 151

📌 PAGA Explained: Graphical Abstractions of Single-Cell Data 🗂 Category: DATA VISUALIZATION 🕒 Date: 2024-06-06 | ⏱️ Read time: 7 min read How a broader view of data can give us insights to its deeper meaning.

40 151

📌 My 30-Day Map Challenge 2023 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-06 | ⏱️ Read time: 9 min read An overview of selected map topics and algorithms

40 151

📌 Multilingual RAG, Algorithmic Thinking, Outlier Detection, and Other Problem-Solving Highlights 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-06 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

40 151

📌 SageMaker vs Vertex AI for Model Inference 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-06-06 | ⏱️ Read time: 14 min read Comparing the AWS and GCP fully-managed services for ML workflows

40 151

📌 From Code to Insights: Software Engineering Best Practices for Data Analysts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-06 | ⏱️ Read time: 20 min read Top 10 engineering lessons every data analyst should know

40 151

📌 Applied LLM Quantisation with AWS Sagemaker | Analytics.gov 🗂 Category: 🕒 Date: 2024-06-07 | ⏱️ Read time: 19 min read Host production-ready LLMs endpoints at twice the speed but one fifth the cost.

40 151

📌 How LLMs Think 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-07 | ⏱️ Read time: 11 min read Research paper in pills: “Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet”

40 151

📌 YOLO – By Hand 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-07 | ⏱️ Read time: 6 min read A breakdown of the math within YOLO

40 151

📌 Fraud Prediction with Machine Learning in the Financial Industry: A Data Scientist’s Experience 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-07 | ⏱️ Read time: 6 min read Insights and experiences from a data scientist on the frontlines

40 151

📌 Automating Prompt Engineering with DSPy and Haystack 🗂 Category: 🕒 Date: 2024-06-07 | ⏱️ Read time: 10 min read Teach your LLM how to talk through examples

40 151

📌 AI Assistants, Copilots, and Agents in Data & Analytics: What’s the Difference? 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-06-07 | ⏱️ Read time: 8 min read Understanding AI autonomy: assistants, copilots, agents, and their impact on business value

40 151

📌 Scale Is All You Need for Lip-Sync? 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-07 | ⏱️ Read time: 14 min read Alibaba’s EMO and Microsoft’s VASA-1 are crazy good. Let’s break down how they work.

40 151

📌 Python 3.14 and the End of the GIL 🗂 Category: PROGRAMMING 🕒 Date: 2025-10-18 | ⏱️ Read time: 16 min read Exploring the opportunities and challenges of a GIL-free Python

40 151

📌 Can We Save the AI Economy? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-10-18 | ⏱️ Read time: 23 min read And do we want to?

40 151

📌 How to Build a Generative Search Engine for Your Local Files Using Llama 3 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-06-08 | ⏱️ Read time: 15 min read Use Qdrant, NVidia NIM API, or Llama 3 8B locally for your local GenAI assistant

40 151

📌 What Is a Good Imputation for Missing Values? 🗂 Category: STATISTICS 🕒 Date: 2024-06-08 | ⏱️ Read time: 21 min read My current take on what imputation should be

40 151

📌 Principal Component Analysis Made Easy: A Step-by-Step Tutorial 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 10 min read Implement the PCA algorithm from scratch with Python

40 151

📌 Tiny Time Mixers (TTM): A Powerful Zero-Shot Forecasting Model by IBM 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 11 min read A new lightweight open-source foundation model