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

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Сеть:Machine Learning with Python Сирия230 Технологии и приложения3 371...

📈 Аналитический обзор Telegram-канала Machine Learning

Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 142 подписчиков, занимая 3 371 место в категории Технологии и приложения и 230 место в регионе Сирия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 142 подписчиков.

Согласно последним данным от 26 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 429, а за последние 24 часа — 20, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 1.83%. В первые 24 часа после публикации контент обычно набирает 1.60% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 735 просмотров. В течение первых суток публикация набирает 643 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 2.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как distance, insidead, gpu, learning, degree.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Благодаря высокой частоте обновлений (последние данные получены 27 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

40 142

Подписчики

+2024 часа

+1017 дней

+42930 день

735

Просмотры поста

~ 64324 часа

~ 87748 часов

1.83%

Коэффициент вовлеченности

~ 3

Постов в день

Ads index

beta

Архив постов

40 142

📌 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

40 142

📌 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

40 142

📌 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

40 142

📌 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

40 142

📌 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

40 142

Repost from Machine Learning with Python

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

40 142

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 👍

40 142

📌 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

40 142

📌 Why Most A/B Tests Are Lying to You 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-11 | ⏱️ Read time: 14 min read The 4 statistical sins that invalidate most A/B tests, plus a pre-test checklist and Bayesian… #DataScience #AI #Python

40 142

📌 Spectral Clustering Explained: How Eigenvectors Reveal Complex Cluster Structures 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-11 | ⏱️ Read time: 10 min read Understanding why spectral clustering outperforms K-means #DataScience #AI #Python

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📌 An Intuitive Guide to MCMC (Part I): The Metropolis-Hastings Algorithm 🗂 Category: MATH 🕒 Date: 2026-03-11 | ⏱️ Read time: 14 min read Tired of the AI hype? Let’s talk about the probabilistic algorithms actually driving high-end quantitative… #DataScience #AI #Python

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📌 How the Fourier Transform Converts Sound Into Frequencies 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-11 | ⏱️ Read time: 26 min read A visual, intuition-first guide to understanding what the math is really doing — from winding… #DataScience #AI #Python

40 142

Repost from Machine Learning with Python

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📌 When Data Lies: Finding Optimal Strategies for Penalty Kicks with Game Theory 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-10 | ⏱️ Read time: 9 min read A data-driven introduction to game theory, Nash equilibrium, and strategic decision-making #DataScience #AI #Python