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 329...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 365 subscribers, ranking 3 329 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 40 365 subscribers.

According to the latest data from 11 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 393 over the last 30 days and by 17 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.29%. Within the first 24 hours after publication, content typically collects 1.74% reactions from the total number of subscribers.
Post reach: On average, each post receives 924 views. Within the first day, a publication typically gains 702 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
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 12 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.

40 365

Subscribers

+1724 hours

+1237 days

+39330 days

924

Post views

~ 70224 hours

~ 1 07048 hours

2.29%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

40 373

📌 Introducing Google’s LangExtract tool 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-08-11 | ⏱️ Read time: 12 min read Do RAG without doing RAG with this powerful new NLP and data extraction library

40 373

📌 Estimating from No Data: Deriving a Continuous Score from Categories 🗂 Category: DATA SCIENCE 🕒 Date: 2025-08-11 | ⏱️ Read time: 13 min read A walk-through of and the maths behind using low-capacity networks to acquire fine-grained scoring when…

40 373

📌 Fine-Tune Your Topic Modeling Workflow with BERTopic 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-08-12 | ⏱️ Read time: 7 min read Learn how to fine-tune BERTopic settings for more focused, reproducible, and interpretable results

40 373

📌 A Refined Training Recipe for Fine-Grained Visual Classification 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-08-12 | ⏱️ Read time: 17 min read How FGVC aims to recognize images belonging to multiple subordinate categories of a super-category

40 373

📌 Coconut: A Framework for Latent Reasoning in LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-08-12 | ⏱️ Read time: 12 min read Explaining Coconut (Training Large Language Models to Reason in a Continuous Latent Space) in simple…

40 373

📌 Model Predictive Control Basics 🗂 Category: DATA SCIENCE 🕒 Date: 2025-08-12 | ⏱️ Read time: 9 min read A hands-on tutorial with Python and CasADi

40 373

📌 Reducing Time to Value for Data Science Projects: Part 4 🗂 Category: DATA SCIENCE 🕒 Date: 2025-08-12 | ⏱️ Read time: 11 min read Embrace your inner software developer

40 373

📌 A Bird’s-Eye View of Linear Algebra: Why Is Matrix Multiplication Like That? 🗂 Category: MATH 🕒 Date: 2025-08-13 | ⏱️ Read time: 21 min read Since the way we manipulate high-dimensional vectors is primarily matrix multiplication, it isn’t a stretch…

40 373

📌 Tips for Setting Expectations in AI Projects 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-08-13 | ⏱️ Read time: 8 min read If you want your AI project to succeed, mastering expectation management comes first. When working…

40 373

📌 Data Mesh Diaries: Realities from Early Adopters 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-08-13 | ⏱️ Read time: 7 min read Early-adopter realities gathered from real data mesh implementations

40 373

📌 How to Use LLMs for Powerful Automatic Evaluations 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-08-13 | ⏱️ Read time: 7 min read A beginner-friendly introduction to LLM-as-a-Judge

40 373

📌 “My biggest lesson was realizing that domain expertise matters more than algorithmic complexity.“ 🗂 Category: AUTHOR SPOTLIGHTS 🕒 Date: 2025-08-14 | ⏱️ Read time: 8 min read Claudia Ng reflects on real-world ML lessons, mentoring newcomers, and her journey from corporate ML…

40 373

📌 What Does “Following Best Practices” Mean in the Age of AI? 🗂 Category: THE VARIABLE 🕒 Date: 2025-08-14 | ⏱️ Read time: 3 min read How data and ML practitioners should navigate a rapidly changing landscape

40 373

📌 LangGraph 101: Let’s Build A Deep Research Agent 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-08-14 | ⏱️ Read time: 32 min read Learn LangGraph fundamentals from Google’s open-source full-stack implementation

40 373

📌 How to Create Powerful LLM Applications with Context Engineering 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-08-18 | ⏱️ Read time: 7 min read Improve your LLM by optimizing its context

40 373

📌 How to Correctly Apply Limits on the Result in DAX (and SQL) 🗂 Category: DATA SCIENCE 🕒 Date: 2025-08-18 | ⏱️ Read time: 8 min read What if the output of a measure mustn’t be above a specific limit? How can…

40 373

📌 Maximizing AI/ML Model Performance with PyTorch Compilation 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-08-18 | ⏱️ Read time: 31 min read Since its inception in PyTorch 2.0 in March 2023, the evolution of torch.compile has been one of…

40 373

📌 Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-06 | ⏱️ Read time: 10 min read A guide to building modular workflows for structured intelligence

40 373

📌 Modular Arithmetic in Data Science 🗂 Category: DATA SCIENCE 🕒 Date: 2025-08-18 | ⏱️ Read time: 10 min read Modular arithmetic is a mathematical system where numbers cycle back to the beginning after reaching…

40 373

📌 Can LangExtract Turn Messy Clinical Notes into Structured Data? 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-08-18 | ⏱️ Read time: 7 min read Turning raw clinical notes into structured entities with LLMs.