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Machine Learning with Python

Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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📈 Análisis del canal de Telegram Machine Learning with Python

El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 813 suscriptores, ocupando la posición 2 416 en la categoría Educación y el puesto 5 038 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 813 suscriptores.

Según los últimos datos del 09 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 70, y en las últimas 24 horas de 10, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.94%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.44% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 1 997 visualizaciones. En el primer día suele acumular 1 652 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 7.
  • Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 10 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 Educación.

67 813
Suscriptores
+1024 horas
+127 días
+7030 días
Archivo de publicaciones
Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813
Self-attention in LLMs, clearly explained #SelfAttention #LLMs #Transformers #NLP #DeepLearning #MachineLearning #AIExplained
Self-attention in LLMs, clearly explained
#SelfAttention #LLMs #Transformers #NLP #DeepLearning #MachineLearning #AIExplained #AttentionMechanism #AIConcepts #AIEducation
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Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813

Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813
Supervised Learning: Classification and Regression Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/
Supervised Learning: Classification and Regression Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
#SupervisedLearning #MachineLearning #Classification #Regression #MLNotes #DataScience #AIResources #MLTheory #MLLectures #LearnML
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Machine Learning with Python
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67 813

Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813

Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813

Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813

Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813

Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813

Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813
Anyone trying to deeply understand Large Language Models. Checkout Foundations of Large Language Models by Tong Xiao & Jingbo
Anyone trying to deeply understand Large Language Models. Checkout
Foundations of Large Language Models
by Tong Xiao & Jingbo Zhu. It’s one of the clearest, most comprehensive resource. ⭐️ Paper Link: arxiv.org/pdf/2501.09223
#LLMs #LargeLanguageModels #AIResearch #DeepLearning #MachineLearning #AIResources #NLP #AITheory #FoundationModels #AIUnderstanding

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Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
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Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813
📖 A comprehensive cheat sheet for working with Polars 🌟 Have you ever worked with pandas and thought that was the fastest way? I thought the same thing until I worked with Polars. ✏️ This cheat sheet explains everything about Polars in a concise and simple way. Not just theory! But also a bunch of real examples, practical experience, and projects that will really help you in the real world. ┌ 🐻‍❄️ Polars Cheat Sheet ├ ♾️ Google Colab └ 📖 Doc
#Polars #DataEngineering #PythonLibraries #PandasAlternative #PolarsCheatSheet #DataScienceTools #FastDataProcessing #GoogleColab #DataAnalysis #PythonForDataScience

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Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
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👫 Preparing for Data Science Interviews 👨🏻‍💻 I've been collecting a variety of data science interview questions for diffe
👫 Preparing for Data Science Interviews 👨🏻‍💻 I've been collecting a variety of data science interview questions for different positions for a few weeks now. ✅ I covered everything, from basic to advanced:
Common Data Science and ML Questions (34 questions)
Regression (22 questions)
Classification (39 questions)
SVM algorithms, decision tree
Simple Bayes and statistical discussions and...
🚨 This list is regularly updated and categorized so that you can easily prepare for the interview step by step.👇 ┌📝 Interview Questions └🐱 GitHub-Repos
#DataScience #InterviewPrep #MLInterviews #DataScientist #MachineLearning #TechCareers #DSInterviewQuestions #GitHubResources #CareerInDataScience #CodingInterview
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Machine Learning with Python
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🔥 Accelerate Your IT Career with FREE Certification Kits! 🚀 Get Hired Faster—Zero Cost! Grab expert guides, labs, and cours
🔥 Accelerate Your IT Career with FREE Certification Kits! 🚀 Get Hired Faster—Zero Cost! Grab expert guides, labs, and courses for AWS, Azure, AI, Python, Cyber Security, and beyond—100% FREE, no hidden fees! ✅ CLICK your field👇 ✅ DOWNLOAD & dominate your goals! 🔗 AWS + Azure Cloud Mastery: https://bit.ly/44S0dNS 🔗 AI & Machine Learning Starter Kit: https://bit.ly/3FrKw5H 🔗 Python, Excel, Cyber Security Courses: https://bit.ly/4mFrA4g 📘 FREE Career Hack: IT Success Roadmap E-book ➔ https://bit.ly/3Z6JS49 🚨 Limited Time! Act FAST! 📱 Join Our IT Study Group: https://bit.ly/43piMq8 💬 1-on-1 Exam Help: https://wa.link/sbpp0m Your dream job won’t wait—GRAB YOUR RESOURCES NOW! 💻✨

Machine Learning with Python
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67 813
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

Machine Learning with Python
Machine Learning with Pythonverified-telegram-icon
67 813
🔥 How to become a data scientist in 2025? 1️⃣ First of all, strengthen your foundation (math and statistics) . ✏️ If you don
🔥 How to become a data scientist in 2025? 1️⃣ First of all, strengthen your foundation (math and statistics) . ✏️ If you don't know math, you'll run into trouble wherever you go. Every model you build, every analysis you do, there's a world of math behind it. You need to know these things well: ✅ Linear Algebra: Link ✅ Calculus: Link ✅ Statistics and Probability: Link ➖➖➖➖➖➖ 2️⃣ Then learn programming ! ✏️ Without further ado, get started learning Python and SQL. ✅ Python: Link ✅ SQL language: Link ✅ Data Structures and Algorithms: Link ➖➖➖➖➖➖ 3️⃣ Learn to clean and analyze data! ✏️ Data is always messy, and a data scientist must know how to organize it and extract insights from it. ✅ Data cleansing: Link ✅ Data visualization: Link ➖➖➖➖➖➖ 4️⃣ Learn machine learning ! ✏️ Once you've mastered the basic skills, it's time to enter the world of machine learning. Here's what you need to know: ◀️ Supervised learning: regression, classification ◀️ Unsupervised learning: clustering, dimensionality reduction ◀️ Deep learning: neural networks, CNN, RNN ✅ Stanford University CS229 course: Link ➖➖➖➖➖➖ 5️⃣ Get to know big data and cloud computing ! ✏️ Large companies are looking for people who can work with large volumes of data. ◀️ Big data tools (e.g. Hadoop, Spark, Dask) ◀️ Cloud services (AWS, GCP, Azure) ➖➖➖➖➖➖ 6️⃣ Do a real project and build a portfolio ! ✏️ Everything you've learned so far is worthless without a real project! ◀️ Participate in Kaggle and work with real data. ◀️ Do a project from scratch (from data collection to model deployment) ◀️ Put your code on GitHub. ✅ Open Source Data Science Projects: Link ➖➖➖➖➖➖ 7️⃣ It's time to learn MLOps and model deployment! ✏️ Many people just build models but don't know how to deploy them. But companies want someone who can put the model into action! ◀️ Machine learning operationalization (monitoring, updating models) ◀️ Model deployment tools: Flask, FastAPI, Docker ✅ Stanford University MLOps Course: Link ➖➖➖➖➖➖ 8️⃣ Always stay up to date and network! ✏️ Follow research articles on arXiv and Google Scholar. ✅ Papers with Code website: link ✅ AI Research at Google website: link
#DataScience #HowToBecomeADataScientist #ML2025 #Python #SQL #MachineLearning #MathForDataScience #BigData #MLOps #DeepLearning #AIResearch #DataVisualization #PortfolioProjects #CloudComputing #DSCareerPath

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Machine Learning with Python
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🚀 Master Python with Ease! I've just compiled a set of clean and powerful Python Cheat Sheets to help beginners and intermediates speed up their coding workflow. Whether you're brushing up on the basics or diving into data science, these sheets will save you time and boost your productivity. 📌 Topics Covered: Python Basics Jupyter Notebook Tips Importing Libraries NumPy Essentials Pandas Overview Perfect for students, developers, and anyone looking to keep essential Python knowledge at their fingertips. #Python #CheatSheets #PythonTips #DataScience #JupyterNotebook #NumPy #Pandas #MachineLearning #AI #CodingTips #PythonForBeginners 🌟 Join the communities:
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Machine Learning with Python
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🚀 DataCamp has officially partnered with Polars**—a cutting-edge DataFrame library designed for speed and efficiency! To mark this exciting collaboration, **DataCamp is offering free access to its brand-new course *“Introduction to Polars”* for the next 90 days. 🎉 This course is a great opportunity for learners and professionals alike to master data cleaning, transformation, and analysis with Polars' high-performance engine, lazy execution, and powerful groupby operations. Unlock the full potential of data workflows and explore how Polars can supercharge large-scale data processing. 🔗 Start learning now: https://www.datacamp.com/courses/introduction-to-polars
#DataScience #Polars #Python #BigData #DataEngineering #MachineLearning #DataAnalytics #OpenSource #DataCamp #FreeCourse #LearnDataScience
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Machine Learning with Python
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⚡ A beautiful booklet for learning deep learning in a smooth and concise way without diving into the world of complexity. ✅ I highly recommend reading this enjoyable booklet. #DeepLearning #AI #MachineLearning #LearnAI #DeepLearningForBeginners 🌟 Join the communities:
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