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 Сирия231 Технологии и приложения3 384...

📈 Аналитический обзор Telegram-канала Machine Learning

Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 106 подписчиков, занимая 3 384 место в категории Технологии и приложения и 231 место в регионе Сирия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 106 подписчиков.

Согласно последним данным от 24 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 401, а за последние 24 часа — 38, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 1.96%. В первые 24 часа после публикации контент обычно набирает 1.16% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 788 просмотров. В течение первых суток публикация набирает 465 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 25 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.

40 106

Подписчики

+3824 часа

+637 дней

+40130 день

788

Просмотры поста

~ 46524 часа

~ 62048 часов

1.96%

Коэффициент вовлеченности

~ 3

Постов в день

Ads index

beta

Архив постов

40 106

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 🌟

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📌 Layered Architecture for Building Readable, Robust, and Extensible Apps 🗂 Category: SOFTWARE ENGINEERING 🕒 Date: 2026-01-27 | ⏱️ Read time: 11 min read If adding a feature feels like open-heart surgery on your codebase, the problem isn’t bugs,… #DataScience #AI #Python

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📌 From Connections to Meaning: Why Heterogeneous Graph Transformers (HGT) Change Demand Forecasting 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-27 | ⏱️ Read time: 12 min read How relationship-aware graphs turn connected forecasts into operational insight #DataScience #AI #Python

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📌 Data Science as Engineering: Foundations, Education, and Professional Identity 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-27 | ⏱️ Read time: 15 min read Recognize data science as an engineering practice and structure education accordingly. #DataScience #AI #Python

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📌 Going Beyond the Context Window: Recursive Language Models in Action 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-27 | ⏱️ Read time: 24 min read Explore a practical approach to analysing massive datasets with LLMs #DataScience #AI #Python

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📌 How Convolutional Neural Networks Learn Musical Similarity 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-26 | ⏱️ Read time: 13 min read Learning audio embeddings with contrastive learning and deploying them in a real music recommendation app #DataScience #AI #Python

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📌 Ray: Distributed Computing For All, Part 2 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-26 | ⏱️ Read time: 11 min read Deploying and running Python code on cloud-based clusters #DataScience #AI #Python

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📌 How Cursor Actually Indexes Your Codebase 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-26 | ⏱️ Read time: 10 min read Exploring the RAG pipeline in Cursor that powers code indexing and retrieval for coding agents #DataScience #AI #Python

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📌 Causal ML for the Aspiring Data Scientist 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-26 | ⏱️ Read time: 18 min read An accessible introduction to causal inference and ML #DataScience #AI #Python

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Repost from Machine Learning with Python

Data Science Interview questions #DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions https://t.me/CodeProgrammer

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📌 SAM 3 vs. Specialist Models — A Performance Benchmark 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-25 | ⏱️ Read time: 19 min read Why specialized models still hold the 30x speed advantage in production environments #DataScience #AI #Python

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📌 Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-25 | ⏱️ Read time: 11 min read Compare Azure ML and AWS SageMaker for scalable model training, focusing on project setup, permission… #DataScience #AI #Python

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📌 Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-24 | ⏱️ Read time: 25 min read Understand air quality: access the available data, interpret data types, and execute starter codes #DataScience #AI #Python

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Ant AI Automated Sales Robot is an intelligent robot focused on automating lead generation and sales conversion. Its core function simulates human conversation, achieving end-to-end business conversion and easily generating revenue without requiring significant time investment. I. Core Functions: Fully Automated "Lead Generation - Interaction - Conversion" Precise Lead Generation and Human-like Communication: Ant AI is trained on over 20 million real social chat records, enabling it to autonomously identify target customers and build trust through natural conversation, requiring no human intervention. High Conversion Rate Across Multiple Scenarios: Ant AI intelligently recommends high-conversion-rate products based on chat content, guiding customers to complete purchases through platforms such as iFood, Shopee, and Amazon. It also supports other transaction scenarios such as movie ticket purchases and utility bill payments. 24/7 Operation: Ant AI continuously searches for customers and recommends products. You only need to monitor progress via your mobile phone, requiring no additional management time. II. Your Profit Guarantee: Low Risk, High Transparency, Zero Inventory Pressure, Stable Commission Sharing We have established partnerships with platforms such as Shopee and Amazon, which directly provide abundant product sourcing. You don't need to worry about inventory or logistics. After each successful order, the company will charge the merchant a commission and share all profits with you. Earnings are predictable and withdrawals are convenient. Member data shows that each bot can generate $30 to $100 in profit per day. Commission income can be withdrawn to your account at any time, and the settlement process is transparent and open. Low Initial Investment Risk. Bot development and testing incur significant costs. While rental fees are required, in the early stages of the project, the company prioritizes market expansion and brand awareness over short-term profits. If you are interested, please join my Telegram group for more information and leave a message: https://t.me/+lVKtdaI5vcQ1ZDA1

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📌 How to Build a Neural Machine Translation System for a Low-Resource Language 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-24 | ⏱️ Read time: 15 min read An introduction to neural machine translation #DataScience #AI #Python

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📌 From Transactions to Trends: Predict When a Customer Is About to Stop Buying 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-23 | ⏱️ Read time: 7 min read Customer churn is usually a gradual process, not a sudden event. In this post, we… #DataScience #AI #Python

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📌 Why the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research by Anthropic Found 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-23 | ⏱️ Read time: 9 min read How prompt engineering has evolved, examined scientifically; and implications for the future of conversational AI… #DataScience #AI #Python

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📌 Achieving 5x Agentic Coding Performance with Few-Shot Prompting 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-23 | ⏱️ Read time: 9 min read Learn to leverage few-shot prompting to increase your LLMs performance #DataScience #AI #Python