¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
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
Mostrar más📈 Análisis del canal de Telegram Machine Learning
El canal Machine Learning (@machinelearning9) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 40 123 suscriptores, ocupando la posición 3 380 en la categoría Tecnologías y Aplicaciones y el puesto 231 en la región Siria.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 123 suscriptores.
Según los últimos datos del 25 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 395, y en las últimas 24 horas de 12, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.89%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.31% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 758 visualizaciones. En el primer día suele acumular 525 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
- Intereses temáticos: El contenido se centra en temas clave como distance, insidead, gpu, learning, degree.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 26 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
40 123
Suscriptores
+1224 horas
+697 días
+39530 días
Archivo de publicaciones
40 128
📌 How to Create an ML-Focused Newsletter
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-08 | ⏱️ Read time: 7 min read
Learn how to make a newsletter with AI tools
#DataScience #AI #Python
40 128
📌 The AI Bubble Will Pop — And Why That Doesn’t Matter
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-08 | ⏱️ Read time: 7 min read
How history’s biggest tech bubble explains where AI is headed next
#DataScience #AI #Python
40 128
🤖🧠 Distil-Whisper: Faster, Smaller, and Smarter Speech Recognition by Hugging Face
🗓️ 08 Dec 2025
📚 AI News & Trends
The evolution of Automatic Speech Recognition (ASR) has reshaped how humans interact with technology. From dictation tools and live transcription to smart assistants and media captioning, ASR technology continues to bridge the gap between speech and digital communication. However, achieving real-time, high-accuracy transcription often comes at the cost of heavy computational requirements until now. Enter ...
#DistilWhisper #FasterSpeechRecognition #SmallerModels #HuggingFace #ASRTechnology #RealTimeTranscription
40 128
📌 The Machine Learning “Advent Calendar” Day 8: Isolation Forest in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-08 | ⏱️ Read time: 11 min read
Isolation Forest may look technical, but its idea is simple: isolate points using random splits.…
#DataScience #AI #Python
40 128
🚀 Master Data Science & Programming!
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
🔰 Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
🔖 Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM
🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
🧑🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
🖊 Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.me/Python53
⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
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Admin: @HusseinSheikho
40 128
📌 Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-07 | ⏱️ Read time: 12 min read
Understanding AI in 2026 — from machine learning to generative models
#DataScience #AI #Python
40 128
It’s common to see normalization and standardization used as if they were the same thing, especially because both are often grouped under the generic name “normalization.”
But they have important differences, and choosing the right one can significantly impact model performance.
Even though both techniques are similar, their goal is the same: reduce scale disparities between variables.
For example, a “salary” feature ranging from 10,000 to 1,000,000 can negatively affect certain algorithms.
Distance-based models like K-means and KNN are highly sensitive to scale.
And in algorithms like Linear Regression and Logistic Regression, large differences in variable scale can mislead the model.
That’s why these preprocessing techniques matter so much.
▫️ When to Normalize (MinMaxScaler)
Normalization is useful when:
It makes sense for values to be between 0 and 1, or within a specific interval;
Variables have very different ranges and don’t follow a normal distribution;
You're using algorithms that are sensitive to scale, such as distance-based methods.
▫️ When to Standardize (StandardScaler)
Standardization is ideal when:
The data has no natural bounds and doesn’t need to be between 0 and 1;
You want zero mean and unit variance;
Variables follow (or approximate) a normal distribution;
You use models like Linear Regression, Logistic Regression or PCA.
In short
Standardization: centers the data around mean 0 and std 1, preserving distribution shape.
Normalization: rescales values into a specific interval (usually 0–1), changing the scale without preserving the original distribution.
40 128
I'm pleased to invite you to join my private Signal group.
All my resources will be free and unrestricted there. My goal is to build a clean community exclusively for smart programmers, and I believe Signal is the most suitable platform for this (Signal is the second most popular app after WhatsApp in the US), making it particularly suitable for us as programmers.
https://signal.group/#CjQKIPcpEqLQow53AG7RHjeVk-4sc1TFxyym3r0gQQzV-OPpEhCPw_-kRmJ8LlC13l0WiEfp
40 128
📌 The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-07 | ⏱️ Read time: 8 min read
In Day 6, we saw how a Decision Tree Regressor finds its optimal split by…
#DataScience #AI #Python
40 128
📌 How to Climb the Hidden Career Ladder of Data Science
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-07 | ⏱️ Read time: 14 min read
The behaviors that get you promoted
#DataScience #AI #Python
40 128
Generating Fake Data in Python!
Instead of spending time coming up with test data — everything can be generated automatically using the
Faker library.
Installing the library:
pip install fakerfrom faker import Faker
# Specify the localization
fake = Faker('ru_RU')print(fake.name())
print(fake.address().replace('\n', ', '))
print(fake.text(max_nb_chars=200))
print(fake.email())
print(fake.country())for _ in range(5):
print({
"name": fake.name(),
"email": fake.email(),
"address": fake.address().replace('\n', ', '),
"lat": float(fake.latitude()),
"lon": float(fake.longitude()),
"website": fake.url()
})40 128
📌 How We Are Testing Our Agents in Dev
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-06 | ⏱️ Read time: 5 min read
Testing that your AI agent is performing as expected is not easy. Here are a…
#DataScience #AI #Python
40 128
🤖🧠 Whisper by OpenAI: The Revolution in Multilingual Speech Recognition
🗓️ 25 Nov 2025
📚 AI News & Trends
Speech recognition has evolved rapidly over the past decade, transforming the way we interact with technology. From voice assistants to transcription services and real-time translation tools, the ability of machines to understand human speech has redefined accessibility, communication and automation. However, one of the major challenges that persisted for years was achieving robust, multilingual and ...
#Whisper #MultilingualSpeechRecognition #OpenAI #SpeechRecognition #AIAccessibility #VoiceTechnology
40 128
🤖🧠 Omnilingual ASR: Meta’s Breakthrough in Multilingual Speech Recognition for 1600+ Languages
🗓️ 24 Nov 2025
📚 AI News & Trends
In an increasingly connected world, speech technology plays a vital role in bridging communication gaps across languages and cultures. Yet, despite rapid progress in Automatic Speech Recognition (ASR), most commercial systems still cater to only a few dozen major languages. Billions of people who speak lesser-known or low-resource languages remain excluded from the benefits of ...
#OmnilingualASR #MultilingualSpeechRecognition #MetaAI #LowResourceLanguages #SpeechTechnology #GlobalCommunication
40 128
🤖🧠 LEANN: The Bright Future of Lightweight, Private, and Scalable Vector Databases
🗓️ 24 Nov 2025
📚 AI News & Trends
In the rapidly expanding world of artificial intelligence, data storage and retrieval efficiency have become major bottlenecks for scalable AI systems. The growth of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) has further intensified the demand for fast, private and space-efficient vector databases. Traditional systems like FAISS or Milvus while powerful, are resource-heavy and ...
#LEANN #LightweightVectorDatabases #PrivateAI #ScalableAI #RAG #AIDataStorage
40 128
🤖🧠 Reducing Hallucinations in Vision-Language Models: A Step Forward with VisAlign
🗓️ 24 Nov 2025
📚 AI News & Trends
As artificial intelligence continues to evolve, Large Vision-Language Models (LVLMs) have revolutionized how machines understand and describe the world. These models combine visual perception with natural language understanding to perform tasks such as image captioning, visual question answering and multimodal reasoning. Despite their success, a major problem persists – hallucination. This issue occurs when a ...
#VisAlign #ReducingHallucinations #VisionLanguageModels #LVLMs #MultimodalAI #AISafety
40 128
📌 The Machine Learning “Advent Calendar” Day 6: Decision Tree Regressor
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-06 | ⏱️ Read time: 10 min read
During the first days of this Machine Learning Advent Calendar, we explored models based on…
#DataScience #AI #Python
40 128
🤖🧠 DeepEyesV2: The Next Leap Toward Agentic Multimodal Intelligence
🗓️ 23 Nov 2025
📚 AI News & Trends
The evolution of artificial intelligence has reached a stage where models are no longer limited to understanding text or images independently. The emergence of multimodal AI systems capable of processing and reasoning across multiple types of data has transformed how machines interpret the world. Yet, most existing multimodal models remain passive observers, unable to act ...
#DeepEyesV2 #AgenticMultimodalIntelligence #MultimodalAI #AIEvolution #ActiveReasoning #AIAction
40 128
Repost from Machine Learning with Python
Our Group on Signal (only for Programmers)
https://signal.group/#CjQKIPcpEqLQow53AG7RHjeVk-4sc1TFxyym3r0gQQzV-OPpEhCPw_-kRmJ8LlC13l0WiEfp
40 128
🤖🧠 Agent-o-rama: The End-to-End Platform Transforming LLM Agent Development
🗓️ 23 Nov 2025
📚 AI News & Trends
As large language models (LLMs) become more capable, developers are increasingly using them to build intelligent AI agents that can perform reasoning, automation and decision-making tasks. However, building and managing these agents at scale is far from simple. Challenges such as monitoring model behavior, debugging reasoning paths, testing reliability and tracking performance metrics can make ...
#AgentoRama #LLMAgents #EndToEndPlatform #AIIntelligence #ModelMonitoring #AIDevelopment
