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

40 150

📌 A Day in the Life of a Data Scientist 🗂 Category: CAREER ADVICE 🕒 Date: 2024-06-08 | ⏱️ Read time: 8 min read What do I actually do all day, anyway?

40 150

📌 Python Data Analysis: What Do We Know About Modern Artists? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 15 min read Finding patterns in the media landscape with Wikipedia, Python, and NetworkX

40 150

📌 Paper review – Communicative Agents for Software Development 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 12 min read After reading and reviewing the Generative Agents paper, I decided to explore the world of…

40 150

📌 SQL Knowledge You Need For Data Science 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 11 min read Topics, resources and advice for becoming proficient in SQL.

40 150

📌 Validating the Causal Impact of the Synthetic Control Method 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 11 min read Causal AI, exploring the integration of causal reasoning into machine learning

40 150

📌 What “Dream Big” Meant for Data Science Innovation at LinkedIn 🗂 Category: BUSINESS 🕒 Date: 2024-06-09 | ⏱️ Read time: 10 min read Here’s how to inspire and lead people for bigger data science projects

40 150

📌 Here is what using an LLM for monsters taught me about programming 🗂 Category: PROGRAMMING 🕒 Date: 2024-06-09 | ⏱️ Read time: 9 min read How I learned to use AI as an alternative to generate amazing random data.

40 150

What do you think about the content of these articles? Useful Content 👍 Unhelpful content 👎

40 150

📌 Hands On Optimization with Expected Improvement and Gaussian Process Regression, in Python 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-09 | ⏱️ Read time: 12 min read A friendly guide to Expected Improvement for Global Optimization, in Python

40 150

📌 Pandas Indexes And Headers, Have You Ever Been Confused? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-09 | ⏱️ Read time: 8 min read From single-level index and headers to multi-level, why and how?

40 150

📌 How LLMs Will Democratize Exploratory Data Analysis 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-09 | ⏱️ Read time: 19 min read Or, When you feel your life’s too hard, just go have a talk with Claude

40 150

📌 It’s Time to Finally Memorize those Dang Classification Metrics! 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-10 | ⏱️ Read time: 11 min read Intuition behind the metrics and how I finally memorized them

40 150

📌 From Masked Image Modeling to Autoregressive Image Modeling 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-10 | ⏱️ Read time: 5 min read A brief review of the image foundation model pre-training objectives

40 150

📌 Building LLM Apps: A Clear Step-By-Step Guide 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-10 | ⏱️ Read time: 14 min read Comprehensive Steps for Building LLM-Native Apps: From Initial Idea to Experimentation, Evaluation, and Productization

40 150

📌 Deploy a LightGBM ML Model With GitHub Actions 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-10 | ⏱️ Read time: 9 min read A beginner’s guide to getting out of Jupyter notebooks and deploying ML models

40 150

📌 How Do Computers Actually Compute? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-10 | ⏱️ Read time: 10 min read A Budding Data Scientist’s Introduction to Computer Hardware

40 150

📌 TDS Newsletter: How to Keep LLMs Effective and Reliable Over Time 🗂 Category: THE VARIABLE 🕒 Date: 2025-10-09 | ⏱️ Read time: 4 min read Those of you who’ve worked with LLM-powered applications know this: by now, building and deploying these tools…

40 150

📌 TDS Newsletter: The Rapid Transformation of Data Science in the Age of AI 🗂 Category: THE VARIABLE 🕒 Date: 2025-10-16 | ⏱️ Read time: 3 min read How data science became a strikingly different discipline in the span of a couple of…

40 150

📌 Statistical Method mcRigor Enhances the Rigor of Metacell Partitioning in Single-Cell Data Analysis 🗂 Category: DATA SCIENCE 🕒 Date: 2025-10-17 | ⏱️ Read time: 6 min read mcRigor detects dubious metacells within each metacell partition and selects the optimal metacell partitioning method…

40 150

📌 How I Used Machine Learning to Predict 41% of Project Delays Before They Happened 🗂 Category: PROJECT MANAGEMENT 🕒 Date: 2025-10-17 | ⏱️ Read time: 12 min read How data science can help project managers anticipate risks and save time