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 Syria232 Technologies & Applications3 398...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 100 subscribers, ranking 3 398 in the Technologies & Applications category and 232 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 100 subscribers.

According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 379 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 1.92%. Within the first 24 hours after publication, content typically collects 1.16% reactions from the total number of subscribers.
Post reach: On average, each post receives 770 views. Within the first day, a publication typically gains 466 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 24 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.

40 100

Subscribers

+3024 hours

+337 days

+37930 days

770

Post views

~ 46624 hours

~ 62048 hours

1.92%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 105

📌 The Proximity of the Inception Score as an Evaluation Criterion 🗂 Category: DEEP LEARNING 🕒 Date: 2026-02-03 | ⏱️ Read time: 7 min read The neighborhood of synthetic data #DataScience #AI #Python

40 105

📌 Building Systems That Survive Real Life 🗂 Category: AUTHOR SPOTLIGHTS 🕒 Date: 2026-02-02 | ⏱️ Read time: 4 min read Sara Nobrega on the transition from data science to AI engineering, using LLMs as a… #DataScience #AI #Python

40 105

📌 Silicon Darwinism: Why Scarcity Is the Source of True Intelligence 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-02 | ⏱️ Read time: 9 min read We are confusing “size” with “smart.” The next leap in artificial intelligence will not come… #DataScience #AI #Python

40 105

📌 Distributed Reinforcement Learning for Scalable High-Performance Policy Optimization 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-02-01 | ⏱️ Read time: 20 min read Leveraging massive parallelism, asynchronous updates, and multi-machine training to match and exceed human-level performance #DataScience #AI #Python

40 105

📌 How to Apply Agentic Coding to Solve Problems 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-31 | ⏱️ Read time: 7 min read Learn how to efficiently solve problems with coding agents #DataScience #AI #Python

40 105

📌 How to Run Claude Code for Free with Local and Cloud Models from Ollama 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-31 | ⏱️ Read time: 16 min read Ollama now offers Anthropic API compatibility #DataScience #AI #Python

40 105

📌 Multi-Attribute Decision Matrices, Done Right 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-30 | ⏱️ Read time: 7 min read How to structure decisions, identify efficient options, and avoid misleading value metrics #DataScience #AI #Python

40 105

📌 On the Possibility of Small Networks for Physics-Informed Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-30 | ⏱️ Read time: 20 min read A new kind of hyperparameter study #DataScience #AI #Python

40 105

📌 Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents” 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-30 | ⏱️ Read time: 27 min read Hard-won lessons on how to scale agentic systems without scaling the chaos, including a taxonomy… #DataScience #AI #Python

40 105

📌 Creating an Etch A Sketch App Using Python and Turtle 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-30 | ⏱️ Read time: 7 min read A beginner-friendly Python tutorial #DataScience #AI #Python

40 105

📌 Randomization Works in Experiments, Even Without Balance 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-29 | ⏱️ Read time: 10 min read Randomization usually balances confounders in experiments, but what happens when it doesn’t? #DataScience #AI #Python

40 105

📌 The Unbearable Lightness of Coding 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-01-29 | ⏱️ Read time: 9 min read Confessions of a vibe coder #DataScience #AI #Python

40 105

📌 RoPE, Clearly Explained 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-29 | ⏱️ Read time: 8 min read Going beyond the math to build intuition #DataScience #AI #Python

40 105

📌 Optimizing Vector Search: Why You Should Flatten Structured Data 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-29 | ⏱️ Read time: 7 min read An analysis of how flattening structured data can boost precision and recall by up to 20% #DataScience #AI #Python

40 105

📌 Machine Learning in Production? What This Really Means 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-28 | ⏱️ Read time: 10 min read From notebooks to real-world systems #DataScience #AI #Python

40 105

📌 Federated Learning, Part 2: Implementation with the Flower Framework 🗂 Category: FEDERATED LEARNING 🕒 Date: 2026-01-28 | ⏱️ Read time: 11 min read Implementing cross-silo federated learning step by step #DataScience #AI #Python

40 105

📌 Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-28 | ⏱️ Read time: 12 min read Estimating neighborhood-level pedestrian risk from real-world incident data #DataScience #AI #Python

40 105

📌 I Ditched My Mouse: How I Control My Computer With Hand Gestures (In 60 Lines of Python) 🗂 Category: COMPUTER VISION 🕒 Date: 2026-01-28 | ⏱️ Read time: 9 min read A step-by-step guide to building a “Minority Report”-style interface using OpenCV and MediaPipe #DataScience #AI #Python

40 105

❗️LISA HELPS EVERYONE EARN MONEY!$29,000 HE'S GIVING AWAY TODAY! Everyone can join his channel and make money! He gives away from $200 to $5.000 every day in his channel https://t.me/+HDFF3Mo_t68zNWQy ⚡️FREE ONLY FOR THE FIRST 500 SUBSCRIBERS! FURTHER ENTRY IS PAID! 👆👇 https://t.me/+HDFF3Mo_t68zNWQy

40 105

Repost from Machine Learning with Python

💛 Top 10 Best Websites to Learn Machine Learning ⭐️ by [@codeprogrammer] --- 🧠 Google’s ML Course 🔗 https://developers.google.com/machine-learning/crash-course 📈 Kaggle Courses 🔗 https://kaggle.com/learn 🧑‍🎓 Coursera – Andrew Ng’s ML Course 🔗 https://coursera.org/learn/machine-learning ⚡️ Fast.ai 🔗 https://fast.ai 🔧 Scikit-Learn Documentation 🔗 https://scikit-learn.org 📹 TensorFlow Tutorials 🔗 https://tensorflow.org/tutorials 🔥 PyTorch Tutorials 🔗 https://docs.pytorch.org/tutorials/ 🏛️ MIT OpenCourseWare – Machine Learning 🔗 https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/ ✍️ Towards Data Science (Blog) 🔗 https://towardsdatascience.com --- 💡 Which one are you starting with? Drop a comment below! 👇 #MachineLearning #LearnML #DataScience #AI https://t.me/CodeProgrammer 🌟