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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Red:📈 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 68 138 suscriptores, ocupando la posición 2 365 en la categoría Educación y el puesto 4 731 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 138 suscriptores.
Según los últimos datos del 31 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 80, y en las últimas 24 horas de 1, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.09%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.54% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 784 visualizaciones. En el primer día suele acumular 1 052 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
- 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 01 septiembre, 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.
68 138
Suscriptores
+124 horas
-97 días
+8030 días
Archivo de publicaciones
Anyone trying to deeply understand Large Language Models.
Checkout
Foundations of Large Language Modelsby Tong Xiao & Jingbo Zhu. It’s one of the clearest, most comprehensive resource. ⭐️ Paper Link: arxiv.org/pdf/2501.09223
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📖 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
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👫 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
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Repost from Machine Learning with Python
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
🔥 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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🚀 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.
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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.
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#DataScience #Polars #Python #BigData #DataEngineering #MachineLearning #DataAnalytics #OpenSource #DataCamp #FreeCourse #LearnDataScience🌟 Join the communities:
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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
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Repost from Machine Learning
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A Gentle Introduction to Word Embedding and Text Vectorization
Ready to read: https://machinelearningmastery.com/a-gentle-introduction-to-word-embedding-and-text-vectorization/
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Yes, I made my own crypto — and now you can too. It’s fun, absurd, and the perfect way to flex in front of your friends with something totally unexpected. I’ll show you exactly how to do it — no tech skills needed.
I kept getting the same questions — so I turned them into a mini FAQ:
💰 Can I pay taxes with my crypto?
- You can try. Let me know how the government reacts.
🧥 Will I need to wear a hoodie and speak in techno-jargon?
- Optional — but it might boost your crypto’s street cred.
🖼️ Can I frame it and hang it on my wall?
- Sure — print out the logo and call it a digital minimalist art.
💣 What if I accidentally create the next Bitcoin?
- Oops. Guess you’ll have to deal with fame and Lambos.
🚀 Will Elon Musk notice my crypto?
- Only if it’s about space, dogs, or memes. No promises.
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🎯 ابدأ رحلتك الاحترافية في البرمجة مع
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هل ترغب بتعلم لغة البرمجة الأكثر طلبًا في العالم؟
هل تحلم بالوصول إلى مجالات مثل الذكاء الاصطناعي، تحليل البيانات أو تصميم الواجهات؟
📢 هذه الدورة خُصصت لتكون نقطة انطلاقك نحو المستقبل!
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🚀 ماذا ستتعلم في هذه الدورة؟
🔹 الوحدة 1: أساسيات بايثون (المتغيرات – أنواع البيانات – العمليات – أساسيات الكود)
🔹 الوحدة 2: التحكم في سير البرنامج (الشروط – الحلقات – أوامر التحكم)
🔹 الوحدة 3: هياكل البيانات (قوائم – قواميس – مجموعات – Tuples)
🔹 الوحدة 4: الدوال (إنشاء – معاملات – النطاق – التكرار)
🔹 الوحدة 5: الوحدات (Modules)
🔹 الوحدة 6: التعامل مع الملفات وملفات CSV
🔹 الوحدة 7: معالجة الاستثناءات باحتراف
🔹 الوحدة 8: البرمجة الكائنية (OOP)
🔹 الوحدة 9: المفاهيم المتقدمة:
✅ المولدات (Generators)
✅ الكائنات القابلة للتكرار (Iterators)
✅ المزينات (Decorators)
💡 عند انتهائك ستكون قادرًا على:
✔️ بناء مشاريع حقيقية بلغة بايثون
✔️ الانتقال بثقة إلى مجالات متقدمة مثل الذكاء الاصطناعي وتحليل البيانات
✔️ أتمتة المهام والتعامل مع البيانات باحتراف
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📅 تاريخ البداية:15- 6
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📕 Mastering NumPy – From Basics to Advanced
NumPy is an essential library in the world of data science, widely recognized for its efficiency in numerical computations and data manipulation. This powerful tool simplifies complex operations with arrays, offering a faster and cleaner alternative to traditional Python lists and loops.
The "Mastering NumPy" booklet provides a comprehensive walkthrough—from array creation and indexing to mathematical/statistical operations and advanced topics like reshaping and stacking. All concepts are illustrated with clear, beginner-friendly examples, making it ideal for anyone aiming to boost their data handling skills.
#NumPy #Python #DataScience #MachineLearning #AI #BigData #DeepLearning #DataAnalysis🌟 Join the communities:
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Scientific Visualization: Python + Matplotlib
📚 Download: https://inria.hal.science/hal-03427242/document
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
