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 Сирия227 Технологии и приложения3 364...

📈 Аналитический обзор Telegram-канала Machine Learning

Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 145 подписчиков, занимая 3 364 место в категории Технологии и приложения и 227 место в регионе Сирия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 145 подписчиков.

Согласно последним данным от 27 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 412, а за последние 24 часа — 5, 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 1.89% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 785 просмотров. В течение первых суток публикация набирает 760 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 28 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.

40 145

Подписчики

+524 часа

+1067 дней

+41230 день

785

Просмотры поста

~ 76024 часа

~ 1 06948 часов

1.96%

Коэффициент вовлеченности

~ 3

Постов в день

Ads index

beta

Архив постов

40 145

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

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📌 Optimizing Data Transfer in Distributed AI/ML Training Workloads 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-01-23 | ⏱️ Read time: 15 min read A deep dive on data transfer bottlenecks, their identification, and their resolution with the help… #DataScience #AI #Python

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📌 What Other Industries Can Learn from Healthcare’s Knowledge Graphs 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-22 | ⏱️ Read time: 11 min read How shared meaning, evidence, and standards create durable semantic infrastructure #DataScience #AI #Python

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📌 Stop Writing Messy Boolean Masks: 10 Elegant Ways to Filter Pandas DataFrames 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-22 | ⏱️ Read time: 7 min read Master the art of readable, high-performance data selection using .query(), .isin(), and advanced vectorized logic. #DataScience #AI #Python

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📌 Why SaaS Product Management Is the Best Domain for Data-Driven Professionals in 2026 🗂 Category: PRODUCT MANAGEMENT 🕒 Date: 2026-01-22 | ⏱️ Read time: 14 min read How I use analytics, automation, and AI to build better SaaS #DataScience #AI #Python

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📌 Evaluating Multi-Step LLM-Generated Content: Why Customer Journeys Require Structural Metrics 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-22 | ⏱️ Read time: 13 min read How to evaluate goal-oriented content designed to build engagement and deliver business results, and why… #DataScience #AI #Python

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📌 A Case for the T-statistic 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-21 | ⏱️ Read time: 21 min read And how it compares to the run-of-the-mill z-score #DataScience #AI #Python

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Guide to AI Coding Agents & Assistants: How to Choose the Right One There are now so many AI tools for coding that it can be confusing to know which one to pick. Some act as simple helpers (Assistant), while others can do the work for you (Agent). This guide breaks down the top AI coding tools that you should be aware of. We will look at what they do, who they are for, and how much they cost. Read: https://habr.com/en/articles/979402/ https://t.me/DataScienceM

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📌 Building a Self-Healing Data Pipeline That Fixes Its Own Python Errors 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-01-21 | ⏱️ Read time: 8 min read How I built a self-healing pipeline that automatically fixes bad CSVs, schema changes, and weird… #DataScience #AI #Python

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📌 If You Want to Become a Data Scientist in 2026, Do This 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-21 | ⏱️ Read time: 10 min read Learn from my mistakes and fast track your data science career #DataScience #AI #Python

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📌 Google Trends is Misleading You: How to Do Machine Learning with Google Trends Data 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-21 | ⏱️ Read time: 11 min read Google Trends is one of the most widely used tools for analysing human behaviour at… #DataScience #AI #Python