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 Syria225 Technologies & Applications3 332...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 310 subscribers, ranking 3 332 in the Technologies & Applications category and 225 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 310 subscribers.

According to the latest data from 09 July, 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 30 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.23%. Within the first 24 hours after publication, content typically collects 1.95% reactions from the total number of subscribers.
Post reach: On average, each post receives 897 views. Within the first day, a publication typically gains 788 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 10 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.

40 310

Subscribers

+3024 hours

+1067 days

+37830 days

897

Post views

~ 78824 hours

~ 1 02448 hours

2.23%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

40 307

📌 Nine Pico PIO Wats with MicroPython (Part 1) 🗂 Category: PROGRAMMING 🕒 Date: 2025-01-23 | ⏱️ Read time: 19 min read Raspberry Pi programmable IO pitfalls illustrated with a musical example

40 307

📌 Real World Use Cases: Strategies that Will Bridge the Gap Between Development and Productionizing 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-23 | ⏱️ Read time: 9 min read Data science demonstrates its value when applied to practical challenges. This article shares insights gained…

40 307

“I thought I knew wine—until I uncovered the secret behind tasting ‘buttery’ Chardonnays and velvety reds. Turns out, the real magic isn’t on the label… Curious what most wine lovers miss? Discover the truth right here 🍷 #إعلان InsideAds

40 307

📌 Building Successful AI Apps: The Dos and Don’ts 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-23 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

40 307

📌 Simplicity Over Black Boxes 🗂 Category: ANALYTICS 🕒 Date: 2025-01-23 | ⏱️ Read time: 7 min read Turning complex ML models into simple, interpretable rules with Human Knowledge Models for actionable insights…

40 307

📌 The Solar Cycle(s): history, data analysis and trend forecasting. 🗂 Category: ANALYTICS 🕒 Date: 2025-01-23 | ⏱️ Read time: 14 min read A brief article on the Solar Cycles: data analysis and time series forecasting for the…

40 307

📌 On a Time Crunch but Still Want to Learn to Develop Multi-Agent AI? 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-23 | ⏱️ Read time: 15 min read These 3 starter projects only take a weekend (and a few cups of coffee, maybe)

40 307

📌 Apollo and Design Choices of Video Large Multimodal Models (LMMs) 🗂 Category: META 🕒 Date: 2025-01-23 | ⏱️ Read time: 13 min read Let’s Explore Major Design Choices from Meta’s Apollo Paper

40 307

📌 Building Research Agents for Tech Insights 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-13 | ⏱️ Read time: 10 min read Using a controlled workflow, unique data & prompt chaining

40 307

📌 A Derivation and Application of Restricted Boltzmann Machines (2024 Nobel Prize) 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-23 | ⏱️ Read time: 8 min read Investigating Geoffrey Hinton’s Nobel Prize-winning work and building it from scratch using PyTorch

40 307

📌 Does It Matter That Online Experiments Interact? 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-24 | ⏱️ Read time: 5 min read What interactions do, why they are just like any other change in the environment post-experiment,…

40 307

📌 Multi-Headed Cross Attention – By Hand 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-24 | ⏱️ Read time: 5 min read Hand computing a fundamental component of multimodal models

40 307

📌 Choosing Classification Model Evaluation Criteria 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-25 | ⏱️ Read time: 9 min read Is Recall / Precision better than Sensitivity / Specificity?

40 307

📌 How Cheap Mortgages Transformed Poland’s Real Estate Market 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-25 | ⏱️ Read time: 19 min read Insights from a synthetic control group

40 307

📌 Optimising Budgets With Marketing Mix Models In Python 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-26 | ⏱️ Read time: 10 min read Part 3 of a hands-on guide to help you master MMM in pymc

40 307

📌 Beyond Causal Language Modeling 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-27 | ⏱️ Read time: 7 min read A deep dive into “Not All Tokens Are What You Need for Pretraining”

40 307

📌 Small Training Dataset? You Need SetFit 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-27 | ⏱️ Read time: 9 min read The enterprise-friendly way to train NLP classifiers with Python in 2025

40 307

📌 Water Cooler Small Talk, Ep 7: Anscombe’s Quartet and the Datasaurus 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-27 | ⏱️ Read time: 10 min read Why descriptive statistics aren’t enough and plotting your data is always essential

40 307

📌 How to Implement Guardrails for Your AI Agents with CrewAI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-27 | ⏱️ Read time: 9 min read LLM Agents are non-deterministic by nature: implement proper guardrails for your AI Application.

40 307

📌 Basics of Probability Notations 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-27 | ⏱️ Read time: 12 min read Union, Intersection, Independence, Disjoint, Complement: Advanced Probability for Data Science Series (1)