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

Red:Machine Learning with Python Siria225 Tecnologías y Aplicaciones3 327...

📈 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 40 373 suscriptores, ocupando la posición 3 327 en la categoría Tecnologías y Aplicaciones y el puesto 225 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 373 suscriptores.

Según los últimos datos del 12 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 399, y en las últimas 24 horas de 24, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.42%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.74% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 979 visualizaciones. En el primer día suele acumular 703 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
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 13 julio, 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.

40 373

Suscriptores

+2424 horas

+1257 días

+39930 días

979

Visitas de la publicación

~ 70324 horas

~ 95648 horas

2.42%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 392

📌 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

40 392

📌 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

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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 faker

Importing and configuring:

from faker import Faker

# Specify the localization
fake = Faker('ru_RU')

Generating basic data:

print(fake.name())
print(fake.address().replace('\n', ', '))
print(fake.text(max_nb_chars=200))
print(fake.email())
print(fake.country())

After running, you will get random values for the name, address, description, email, and country. Generating multiple records:

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()
    })

🔥 Ideal for test filling of databases. A great way to practice working with external libraries and generating data. 🚪 https://t.me/DataScienceM

40 392

📌 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

40 392

🤖🧠 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

40 392

🤖🧠 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

40 392

🤖🧠 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

40 392

🤖🧠 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

40 392

📌 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

40 392

🤖🧠 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

40 392

Repost from Machine Learning with Python

Our Group on Signal (only for Programmers) https://signal.group/#CjQKIPcpEqLQow53AG7RHjeVk-4sc1TFxyym3r0gQQzV-OPpEhCPw_-kRmJ8LlC13l0WiEfp

40 392

🤖🧠 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

40 392

🤖🧠 CALM: Revolutionizing Large Language Models with Continuous Autoregressive Learning 🗓️ 23 Nov 2025 📚 AI News & Trends Large Language Models (LLMs) such as GPT, Claude and Gemini have dramatically transformed artificial intelligence. From generating natural text to assisting in code and research, these models rely on one fundamental process: autoregressive generation predicting text one token at a time. However, this sequential nature poses a critical efficiency bottleneck. Generating text token by token ... #CALM #ContinuousAutoregressiveLearning #LargeLanguageModels #AutoregressiveGeneration #AIEfficiency #AIInnovation

40 392

🤖🧠 Supervised Reinforcement Learning: A New Era of Step-Wise Reasoning in AI 🗓️ 23 Nov 2025 📚 AI News & Trends In the evolving landscape of artificial intelligence, large language models (LLMs) like GPT, Claude and Qwen have demonstrated remarkable abilities from generating human-like text to solving complex problems in mathematics, coding, and logic. Yet, despite their success, these models often struggle with multi-step reasoning, especially when each step depends critically on the previous one. Traditional ... #SupervisedReinforcementLearning #StepWiseReasoning #ArtificialIntelligence #LargeLanguageModels #MultiStepReasoning #AIBreakthrough

40 392

📌 Reading Research Papers in the Age of LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-12-06 | ⏱️ Read time: 10 min read How I keep up with papers with a mix of manual and AI-assisted reading #DataScience #AI #Python

40 392

Repost from Machine Learning with Python

If you want to truly understand how AI systems like #GPT, #Claude, #Llama or #Mistral work at their core, these 85 foundational concepts are essential. The visual below breaks down the most important ideas across the full #AI and #LLM landscape. https://t.me/CodeProgrammer ✅

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📌 The Best Data Scientists are Always Learning 🗂 Category: DATA SCIENCE 🕒 Date: 2025-12-04 | ⏱️ Read time: 7 min read Why continuous learning matters & how to come up with topics to study #DataScience #AI #Python

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📌 Bootstrap a Data Lakehouse in an Afternoon 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-12-04 | ⏱️ Read time: 12 min read Using Apache Iceberg on AWS with Athena, Glue/Spark and DuckDB #DataScience #AI #Python

40 392

📌 Build and Deploy Your First Supply Chain App in 20 Minutes 🗂 Category: PROGRAMMING 🕒 Date: 2025-12-04 | ⏱️ Read time: 21 min read A factory operator that discovered happiness by switching from notebook to streamlit – (Image Generated… #DataScience #AI #Python

40 392

📌 The Machine Learning “Advent Calendar” Day 4: k-Means in Excel 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-12-04 | ⏱️ Read time: 7 min read Discover how to implement the k-Means clustering algorithm, a fundamental machine learning technique, using only Microsoft Excel. This guide, part of a "Machine Learning Advent Calendar" series, walks through building a training algorithm from scratch in a familiar spreadsheet environment, demystifying what "real" ML looks like in practice. #MachineLearning #kMeans #Excel #DataScience #Tutorial