¡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 67 823 suscriptores, ocupando la posición 2 412 en la categoría Educación y el puesto 5 047 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 823 suscriptores.
Según los últimos datos del 08 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 50, y en las últimas 24 horas de -5, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.79%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.60% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 895 visualizaciones. En el primer día suele acumular 1 764 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 09 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.
67 823
Suscriptores
-524 horas
+227 días
+5030 días
Archivo de publicaciones
python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docxfrom docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'
+2
Top 150 Python Interview Questions
This PDF covers the most frequently asked Python questions with answers to help you prepare for upcoming interviews.
🔸Link to PDF
👉 @DataScience4
No skills? No problem. Just copy-paste and GET PAID.
➡️ 22,000+ already started… YOU'RE NEXT! Click here @NPFXSignals
#إعلان InsideAds
🚀 16th AI by Hand ✍️ Workshops, Scholarships available 👉 https://lu.ma/te2q2zqu
Every Wednesday we’ve been bringing people together to learn AI by hand ✍️.
Next week marks our 16th workshop, and thanks to Google’s generous sponsorship, we’re offering scholarships for educators and students to join us.
Choose your session:
🙌 Deep Learning Beginner Math Workshop
Build the math foundation for deep learning:
1. Dot Product
2. Matrix Multiplication
3. Linear Layer
4. Activation
5. Artificial Neuron
🙌 🙌 Transformer in Excel Workshop (Intermediate)
For AI engineers who know how to use Transformers but want to open the black box. We’ll visualize every step—data flow, math, and dimension alignment—inside Excel.
🙌 🙌 🙌 Latest AI Paper Workshop (Advanced)
Work through a just-published model, architecture, or algorithm with brand-new AI by Hand exercises—crafted for this workshop only.
🙌 🙌 🙌 Deep Reinforcement Learning Workshop (Advanced)
From replay buffers to Monte Carlo, TD learning, Deep Q-Networks, and SARSA—understand value-based deep RL from the ground up.
📅 When: Every Wednesday
🎓 Scholarships: Available for educators & students (sponsored by Google)
Register 🔗 https://lu.ma/te2q2zqu
Grab these free AI courses before they get paywalled:
𝟭. Prompt Engineering Basics:
https://skillbuilder.aws/search?searchText=foundations-of-prompt-engineering&showRedirectNotFoundBanner=true
𝟮. ChatGPT Prompts Mastery:
https://deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
𝟯. Intro to Generative AI:
https://cloudskillsboost.google/course_templates/536
𝟰. AI Introduction by Harvard:
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python/2023-05
𝟱. Microsoft GenAI Basics:
https://linkedin.com/learning/what-is-generative-ai/generative-ai-is-a-tool-in-service-of-humanity
𝟲. Prompt Engineering Pro:
https://learnprompting.org
𝟳. Google’s Ethical AI:
https://cloudskillsboost.google/course_templates/554
𝟴. Harvard Machine Learning:
https://pll.harvard.edu/course/data-science-machine-learning
𝟵. LangChain App Developer:
https://deeplearning.ai/short-courses/langchain-for-llm-application-development/
𝟭𝟬. Bing Chat Applications:
https://linkedin.com/learning/streamlining-your-work-with-microsoft-bing-chat
𝟭𝟭. Generative AI by Microsoft:
https://learn.microsoft.com/en-us/training/paths/introduction-to-ai-on-azure/
𝟭𝟮. Amazon’s AI Strategy:
https://skillbuilder.aws/search?searchText=generative-ai-learning-plan-for-decision-makers&showRedirectNotFoundBanner=true
𝟭𝟯. GenAI for Everyone:
https://deeplearning.ai/courses/generative-ai-for-everyone/
𝟭𝟰. AWS GenAI Foundation:
https://coursera.org/learn/generative-ai-with-llms
https://t.me/CodeProgrammer 🔰
Repost from Machine Learning with Python
Special offer available only for the first ten people, access all our paid content for $1.50 per month
Offer available only for the first ten people
https://t.me/+Sg7lfv7C7xtjZGNi
#LSTMs made AI remember before #Transformers took over
here’s the 15-step by-hand ✍️ guide
you can download: https://www.byhand.ai/p/26-lstm
https://t.me/CodeProgrammer
Microsoft launched the best course on Generative AI!
The Free 21 lesson course is available on #Github and will teach you everything you need to know to start building #GenerativeAI applications.
Enroll: https://github.com/microsoft/generative-ai-for-beginners
https://github.com/microsoft/generative-ai-for-beginners 🩷
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
I recommend you to join @TradingNewsIO for Global & Economic News 24/7
⚡️Stay up-to-date with real-time updates on global events.
➡️ Click Here and JOIN NOW !
#إعلان InsideAds
Как узнать, что кожа говорит о тебе?
Твоя усталость может отражаться высыпаниями, стресс — покраснением, а бессонница — сухостью.
Настоящий уход — это забота, а не просто баночки на полке.
Хочешь понять язык своей кожи и настроить рутину так, чтобы видеть реальный результат? Загляни сюда — здесь с тобой говорят на языке тела и чувств, не шаблонов.
#إعلان InsideAds
LangExtract
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
GitHub: https://github.com/google/langextract
https://t.me/DataScience4 🖕
Join our paid channel, content and rare resources for learning, mastering artificial intelligence, data analysis, Python and other resources that you will only find in our channels
https://t.me/+r_Tcx2c-oVU1OWNi
this link for only 7 members
🚀 Model Context Protocol (MCP) Curriculum for Beginners
Learn MCP with Hands-on Code Examples in C#, Java, JavaScript, Python, and TypeScript
🧠 Overview of the Model Context Protocol Curriculum
The Model Context Protocol (MCP) is an innovative framework designed to standardize communication between AI models and client applications. This open-source curriculum provides a structured learning path, featuring practical coding examples and real-world scenarios across popular programming languages such as C#, Java, JavaScript, TypeScript, and Python.
Whether you're an AI developer, system architect, or software engineer, this guide is your all-in-one resource for mastering MCP fundamentals and implementation techniques.
Resources: https://github.com/microsoft/mcp-for-beginners/blob/main/translations/en/README.md
https://t.me/CodeProgrammer ⭐️
https://t.me/InsideAds_bot/open?startapp=r_148350890_utm_source-insideadsInternal-utm_medium-notification-utm_campaign-referralRegistered
if you have channel , make money by using this ads paltform
easy and auto ads posting ( profit: 100$ monthly per channel)
No one believed vineyards could become this high-tech – until now. What I saw at Reservoir Farms changed everything I knew about wine and robotics. Want a glimpse into the future of wine? See the secret here 🍷
#إعلان InsideAds
I turned confusion into confidence with this DL roadmap. Now it’s your turn!
𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 (𝗪𝗲𝗲𝗸 𝟭-𝟮)
● Understand perceptrons, sigmoid, ReLU, tanh
● Learn cost functions, gradient descent, and derivatives
● Implement binary logistic regression using NumPy
𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗦𝗵𝗮𝗹𝗹𝗼𝘄 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗪𝗲𝗲𝗸 𝟯-𝟰)
● Build a neural net with one hidden layer
● Compare activation functions (sigmoid vs tanh vs ReLU)
● Train your model to classify simple images
𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗗𝗲𝗲𝗽 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗪𝗲𝗲𝗸 𝟱-𝟲)
● Forward and backward propagation through multiple layers
● Parameter initialization and tuning
● Implement L-layer neural networks from scratch
𝗣𝗵𝗮𝘀𝗲 𝟰: 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗥𝗲𝗴𝘂𝗹𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗪𝗲𝗲𝗸 𝟳-𝟴)
● Learn mini-batch gradient descent, RMSProp, and Adam
● Apply L2 and Dropout regularization to avoid overfitting
● Boost your model’s performance with better convergence
𝗣𝗵𝗮𝘀𝗲 𝟱: 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄 & 𝗥𝗲𝗮𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗪𝗲𝗲𝗸 𝟵-𝟭𝟬)
● Build models using TensorFlow and Keras
● Normalize data, tune hyperparameters, and visualize metrics
● Create multi-class classifiers using softmax
𝗣𝗵𝗮𝘀𝗲 𝟲: 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 & 𝗖𝗮𝗿𝗲𝗲𝗿 𝗣𝗿𝗲𝗽 (𝗪𝗲𝗲𝗸 𝟭𝟭-𝟭𝟮)
● Work on image recognition, text classification, and real datasets
● Learn model deployment techniques
● Prepare for interviews with hands-on projects and GitHub repo
https://t.me/CodeProgrammer ✉️
I lost 12 kg in 3 months—without crazy workouts or starving myself. No magic pills, no gym membership. Just 3 habits from this simple fitness channel that everyone ignores. Ready to change your body for good? Find out the real secrets here.
#إعلان InsideAds
