¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
Machine Learning with Python
Ir al canal en Telegram
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Mostrar más📈 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 67 819 suscriptores, ocupando la posición 2 404 en la categoría Educación y el puesto 5 049 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 819 suscriptores.
Según los últimos datos del 05 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 77, y en las últimas 24 horas de 9, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.60%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.50% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 767 visualizaciones. En el primer día suele acumular 1 695 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 6.
- 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 06 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.
67 819
Suscriptores
+924 horas
+587 días
+7730 días
Archivo de publicaciones
Build a Club. Win Cash.
TRAIL by PartBuy
Compete, dominate, get paid. Form your crew, win wars, and split $600 with your club. The most competitive game on Telegram.
Ad. 18+
Микро-каналы — главный тренд на рынке телеграма среди рекламодателей в этом году
Канал на пару десятков читателей есть почти у каждого, но где найти клиентов с деньгами?
Ловите главный бот сезона — ADMINOTEKA! Заявки с $$$ сами будут сыпаться к вам каждый день, выбирайте понравившиеся и публикуйте в канале.
Проще уже не будет
Pandas cheat sheet
Use the following Pandas cheat sheet to quickly reference some of the most common operations you might perform with the Pandas library.
More: https://www.coursera.org/resources/pandas-cheat-sheet
200$ to 20k$ SOL Challenge!
As promised, i will do another challenge for those who missed the previous one!
Last one we completed in 6 days, let’s do this one even quicker!
Join my free group Before closing 👇
https://t.me/+DAKLP7eUy9Y3ZjY0
#ad InsideAds
Repost from Learn Python Coding
Python Cheat Sheet: Beginner to Expert Guide
This #Python cheat sheet covers basics to advanced concepts, regex, list slicing, loops and more. Perfect for quick reference and enhancing your coding skills.
Read: https://www.almabetter.com/bytes/cheat-sheet/python
https://t.me/DataScience4 ✉️
Matplotlib Cheat Sheet (Basics to Advanced)
Learn key Matplotlib functions with our Matplotlib cheat sheet. Includes examples, advanced customizations and comparison with Seaborn for better visualizations
Matplotlib is a versatile library in Python used for data visualization. Matplotlib enables the creation of static, interactive, and animated visualizations in Python. It is highly customizable and integrates well with libraries like Pandas and NumPy. Its pyplot module simplifies the process of creating plots similar to MATLAB. This Matplotlib cheat sheet provides an overview of the essential functions, features, and tools available in Matplotlib, along with comparisons to Seaborn where relevant.
Read: https://www.almabetter.com/bytes/cheat-sheet/matplotlib
https://t.me/CodeProgrammer
Analysis of 7,200+ users shows that personalized AI conversation starters increase response rates by 42%. Love Pro - AI Dating Copilot optimizes your messaging with proven linguistic patterns and real-time data. Improve your dating profile based on insights derived from 10,000+ successful interactions. Send a chat screenshot or describe your scenario to receive targeted advice. See how precision-driven communication can elevate your dating outcomes. Start optimizing now: Love Pro.
#ad InsideAds
I spent hours testing AI dating bots so you don’t have to. Love Pro - AI Dating Copilot is the real deal. No fluff, just smart help to start chats, reply fast, and level up your profile. You want results? This bot delivers - instantly. Don’t miss out on your next match. Act NOW before your crush gets snatched! Join me here: Love Pro - AI Dating Copilot and watch your DMs explode!
#ad InsideAds
Your Stress-Free Roadmap to Safe Betting Bonuses
Discover the exclusive promo codes that unlock safe sites with fast payouts.
Feel secure using official APKs vetted for your peace of mind.
Step-by-step: Copy the code → Register easily → Enjoy your bonus without hassle.
Join thousands already winning calmly with trusted platforms.
Claim your comfort zone now → Tested Betting Sites
#ad InsideAds
Build a Club. Win Cash.
TRAIL by PartBuy
Compete, dominate, get paid. Form your crew, win wars, and split $600 with your club. The most competitive game on Telegram.
Ad. 18+
This cheat sheet—part of our Complete Guide to NumPy, pandas, and Data Visualization—offers a handy reference for essential pandas commands, focused on efficient data manipulation and analysis. Using examples from the Fortune 500 Companies Dataset, it covers key pandas operations such as reading and writing data, selecting and filtering DataFrame values, and performing common transformations.
You'll find easy-to-follow examples for grouping, sorting, and aggregating data, as well as calculating statistics like mean, correlation, and summary statistics. Whether you're cleaning datasets, analyzing trends, or visualizing data, this cheat sheet provides concise instructions to help you navigate pandas’ powerful functionality.
Designed to be practical and actionable, this guide ensures you can quickly apply pandas’ versatile data manipulation tools in your workflow.
Sometimes the timing is more important than the prediction. The chart will always go up slowly, you need to exit before it crashes.
#ad InsideAds
Repost from Machine Learning
10 GitHub Repositories to Master System Design
Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.
Most engineers encounter system design when preparing for interviews, but in reality, it is much bigger than that. System design is about understanding how large-scale systems are built, why certain architectural decisions are made, and how trade-offs shape everything from performance to reliability. Behind every app you use daily, from messaging platforms to streaming services, there are careful decisions about databases, caching, load balancing, fault tolerance, and consistency models. What makes system design challenging is that there is rarely a single correct answer. You are constantly balancing cost, scalability, latency, complexity, and future growth. Should you shard the database now or later? Do you prioritize strong consistency or eventual consistency? Do you optimize for reads or writes? These are the kinds of questions that separate surface-level knowledge from real architectural thinking. The good news is that many experienced engineers have documented these patterns, breakdowns, and interview strategies openly on GitHub. Instead of learning only through trial and error, you can study real case studies, curated resources, structured interview frameworks, and production-grade design principles from the community. In this article, we review 10 GitHub repositories that cover fundamentals, interview preparation, distributed systems concepts, machine learning system design, agent-based architectures, and real-world scalability case studies. Together, they provide a practical roadmap for developing the structured thinking required to design reliable systems at scale. Read: https://www.kdnuggets.com/10-github-repositories-to-master-system-design https://t.me/DataScienceM ✅
Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory
Need help choosing the right Python dataframe library? This article compares Pandas and Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
Over 20 free courses are now available on our channel for a very limited time.
https://t.me/DataScienceC
⚡️ MIT has released a full course on Deep Learning - for free
MIT OpenCourseWare has published the course 6.7960 Deep Learning (Fall 2024) — one of the most relevant and practical university courses on modern deep learning.
It includes full-fledged lectures at a top-university level, available for free.
What's in the course
- Fundamentals of deep learning and architectures
- Transformers and modern models
- Generative AI
- Self-supervised learning
- Scaling laws
- Diffusion and generative models
- RL and reinforcement learning
- Practical analyses of modern approaches
The lectures are led by MIT professors and researchers working with cutting-edge technologies.
Why it's valuable
This is not a basic course for beginners.
This is material at the level of:
- ML engineers
- researchers
- developers of AI systems
The course reflects the current state of the industry and explains how people who create modern models think.
It's perfect if you:
- already know Python and the basics of ML
- want to transition to Deep Learning
- work with LLMs / AI
- want a systematic understanding instead of individual tutorials
If you want FAANG / Research-level knowledge - learn from MIT.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/video_galleries/lecture-videos/
https://t.me/CodeProgrammer ✅
💠Hello friends!🎯
We have launched a new Deepfake bot.
The bot allows you to:
🎥 Replace the face in the video with the face from your photo.
🖼 Replace the face in the photo with the face from another photo.
📸 Bring photos to life.
👇👇👇
https://t.me/DeepFakeRro_Bot
https://t.me/DeepFakeRro_Bot
#ad InsideAds
🏆 We’re a trusted vendor with over six years of
experience, more then 10000+ verified sales on the web and now on Telegram.
✈ We have a wide assortment of products, shipping from 🇩🇪 Germany to Europe and green-flagged countries worldwide. Our 🇦🇺 Australia and 🇺🇸 USA shops offer local shipping to avoid import risks.
🔒 We prioritize top-quality products, strict privacy measures, and exceptional customer satisfaction.
ORDER HERE 👉🏼 Worldwide bot 👈🏼
#ad InsideAds
