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

Network:Machine Learning with Python Syria231 Technologies & Applications3 380...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 123 subscribers, ranking 3 380 in the Technologies & Applications category and 231 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 123 subscribers.

According to the latest data from 25 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 395 over the last 30 days and by 12 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 1.89%. Within the first 24 hours after publication, content typically collects 1.31% reactions from the total number of subscribers.
Post reach: On average, each post receives 758 views. Within the first day, a publication typically gains 525 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 26 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

40 123

Subscribers

+1224 hours

+697 days

+39530 days

758

Post views

~ 52524 hours

~ 70348 hours

1.89%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

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📌 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

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📌 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

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🤖🧠 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

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📌 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

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🚀 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 ━━━━━━━━━━━━━━━━━━ Admin: @HusseinSheikho

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📌 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

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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.

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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

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📌 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

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📌 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

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📌 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

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🤖🧠 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

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🤖🧠 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

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🤖🧠 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

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🤖🧠 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

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📌 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

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🤖🧠 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

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Repost from Machine Learning with Python

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

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🤖🧠 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