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 68 102 suscriptores, ocupando la posición 2 372 en la categoría Educación y el puesto 4 808 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 102 suscriptores.
Según los últimos datos del 27 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 112, y en las últimas 24 horas de 8, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.52%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.90% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 077 visualizaciones. En el primer día suele acumular 1 291 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 28 agosto, 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.
68 102
Suscriptores
+824 horas
-727 días
+11230 días
Archivo de publicaciones
Machine Learning in Python (Course Notes)
I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!
Here’s what you’ll learn:
🔘 Linear Regression - The foundation of predictive modeling
🔘 Logistic Regression - Predicting probabilities and classifications
🔘 Clustering (K-Means, Hierarchical) - Making sense of unstructured data
🔘 Overfitting vs. Underfitting - The balancing act every ML engineer must master
🔘 OLS, R-squared, F-test - Key metrics to evaluate your models
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Comment
Посилання на сайт: https://applicationhubs.com/?utm_source=telegram&utm_campaign=collaborator зробити кнопкою з текстом "Спробувати ApplicationHubs"
Publication text
Знайшов цікавий сервіс для розробників — ApplicationHubs.
Це платформа, яка дозволяє запускати повноцінне Linux-середовище розробки у хмарі. Можна створити свій Dev Hub, підключитися через SSH, VSCode Remote або JetBrains Gateway і працювати як на звичайному комп'ютері — тільки без налаштування локального середовища.
Підтримуються Docker-проєкти, будь-які мови та фреймворки.
По суті це персональна cloud development machine, яку можна запустити за кілька секунд.
Зараз відкрито ранній доступ (early access).
[👉 https://applicationhubs.com](https://applicationhubs.com/?utm_source=telegram&utm_campaign=collaborator)
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⏰Last Chance – Get It Before It’s Gone!
Repost from Learn Python Coding
The Ultimate 2026 Python Learning Roadmap: From Beginner to Expert
Start learning #Python in 2026 with a clear, structured #roadmap that takes you from beginner to expert. Build real-world skills through hands-on projects, master essential libraries, and prepare for in-demand careers in data science, web development, and #AI
Start: https://www.coursera.org/resources/python-learning-roadmap
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Repost from Learn Python Coding
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
🧠 Python libraries for AI agents - complexity of learning 🔥
🟢 Easy
• LangChain
• tool calling
• agent memory
• simple agents
• CrewAI
• agents with roles
• collaboration of several agents
• SmolAgents
• lightweight agents
• quick experiments
🟡 Medium
• LangGraph
• stateful workflow
• agent orchestration
• LlamaIndex
• RAG pipelines
• data indexing
• knowledge agents
• OpenAI Agents SDK
• tool integrations
• agent workflows
• Strands
• agent orchestration
• task coordination
• Semantic Kernel
• skills / plugins
• AI process orchestration
• PydanticAI
• typed LLM applications
• structured agent workflows
• Langroid
• message exchange between agents
• interaction with tools
🔴 Difficult
• AutoGen
• multi-agent dialogues
• autonomous agent cooperation
• DSPy
• programmable prompting
• optimization of LLM pipelines
• A2A
• agent-to-agent protocol
• distributed agent systems
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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
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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.
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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.
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