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 40 072 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 40 072 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.

40 072

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

40 071

📌 Hallucinations in LLMs Are Not a Bug in the Data 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-16 | ⏱️ Read time: 10 min read It’s a feature of the architecture #DataScience #AI #Python

40 071

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40 071

📌 Bayesian Thinking for People Who Hated Statistics 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-16 | ⏱️ Read time: 12 min read You already think like a Bayesian. Your stats class just taught the formula before the… #DataScience #AI #Python

40 071

Rocket.new lets you build a full website using prompts with their vibe solutioning platform 🧠⚡️ You describe it, it does the work. 🎁 For the first time on this channel: 100% OFF for 2 months 🛒 Coupon code: X7K2M9P4R1NQ ✔️ Valid on all pricing plans Go to Rocket.new now, enter the code, claim your 2 months free, or miss out and come back later paying the full subscription. 💸 claim your 2 months free

40 071

📌 The Causal Inference Playbook: Advanced Methods Every Data Scientist Should Master 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-15 | ⏱️ Read time: 17 min read Master six advanced causal inference methods with Python: doubly robust estimation, instrumental variables, regression discontinuity,… #DataScience #AI #Python

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📌 The 2026 Data Mandate: Is Your Governance Architecture a Fortress or a Liability? 🗂 Category: DATA GOVERNANCE 🕒 Date: 2026-03-15 | ⏱️ Read time: 8 min read Is your data strategy 2026-ready? Get a deep dive into the mandatory shift toward human-in-the-loop… #DataScience #AI #Python

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📌 The Current Status of The Quantum Software Stack 🗂 Category: QUANTUM COMPUTING 🕒 Date: 2026-03-14 | ⏱️ Read time: 8 min read How do we program quantum computers today? #DataScience #AI #Python

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📌 The Multi-Agent Trap 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-14 | ⏱️ Read time: 12 min read Google DeepMind found multi-agent networks amplify errors 17x. Learn 3 architecture patterns that separate $60M… #DataScience #AI #Python

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📌 Personalized Restaurant Ranking with a Two-Tower Embedding Variant 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-13 | ⏱️ Read time: 6 min read How a lightweight two-tower model improved restaurant discovery when popularity ranking failed #DataScience #AI #Python

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📌 How Vision Language Models Are Trained from “Scratch” 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-13 | ⏱️ Read time: 13 min read A deep dive into exactly how text-only language models are finetuned to see images #DataScience #AI #Python

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📌 Why Care About Prompt Caching in LLMs? 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-13 | ⏱️ Read time: 11 min read Optimizing the cost and latency of your LLM calls with Prompt Caching #DataScience #AI #Python

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Repost from Machine Learning with Python

🗂 Building our own mini-Skynet — a collection of 10 powerful AI repositories from big tech companies 1. Generative AI for Beginners and AI Agents for Beginners Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice. 2. LLMs from Scratch Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood". 3. OpenAI Cookbook An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI. 4. Segment Anything and Stable Diffusion Classic tools for computer vision and image generation from Meta and the CompVis research team. 5. Python 100 Days and Python Data Science Handbook A powerful resource for Python and data analysis. 6. LLM App Templates and ML for Beginners Ready-made app templates with LLMs and a structured course on classic machine learning. If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit. tags: #github #LLM #AI #ML ➡️ https://t.me/CodeProgrammer

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📌 How to Build Agentic RAG with Hybrid Search 🗂 Category: RAG 🕒 Date: 2026-03-13 | ⏱️ Read time: 7 min read Learn how to build a powerful agentic RAG system #DataScience #AI #Python

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📌 A Tale of Two Variances: Why NumPy and Pandas Give Different Answers 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-13 | ⏱️ Read time: 7 min read Imagine you are analyzing a small dataset: You want to calculate some summary statistics to… #DataScience #AI #Python

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📌 I Finally Built My First AI App (And It Wasn’t What I Expected) 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-12 | ⏱️ Read time: 14 min read A beginner-friendly walkthrough of API calls, environment variables, and real-world AI infrastructure #DataScience #AI #Python

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📌 Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-12 | ⏱️ Read time: 11 min read Navigating the performance cliff: How pairing MRL with int8 and binary quantization balances infrastructure costs… #DataScience #AI #Python

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📌 Solving the Human Training Data Problem 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-12 | ⏱️ Read time: 18 min read How AI has completely transformed the way I study as a graduate student #DataScience #AI #Python

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Repost from Machine Learning with Python

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

40 071

Repost from Machine Learning with Python

Machine Learning in Python (Course Notes) I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you! Here’s what you’ll learn: 🔘 Linear Regression - The foundation of predictive modeling 🔘 Logistic Regression - Predicting probabilities and classifications 🔘 Clustering (K-Means, Hierarchical) - Making sense of unstructured data 🔘 Overfitting vs. Underfitting - The balancing act every ML engineer must master 🔘 OLS, R-squared, F-test - Key metrics to evaluate your models https://t.me/CodeProgrammer || Share 🌐 and Like 👍

40 071

📌 Exploratory Data Analysis for Credit Scoring with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-12 | ⏱️ Read time: 16 min read Understanding default risk through statistical analysis of borrower and loan characteristics. #DataScience #AI #Python