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

Network:Machine Learning with Python India5 038 Education2 416...

📈 Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 67 809 subscribers, ranking 2 416 in the Education category and 5 038 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 809 subscribers.

According to the latest data from 09 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 70 over the last 30 days and by 10 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.94%. Within the first 24 hours after publication, content typically collects 2.44% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 997 views. Within the first day, a publication typically gains 1 652 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 7.
Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 10 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

67 809

Subscribers

+1024 hours

+127 days

+7030 days

1 997

Post views

~ 1 65224 hours

~ 2 22648 hours

2.94%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

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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-docx

Example

from 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.'

https://t.me/DataScienceN 🚗

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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

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https://t.me/+r_Tcx2c-oVU1OWNi

67 809

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🚀 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

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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 🔰

67 809

Repost from Machine Learning with Python

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#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

67 809

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 🩷

67 809

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

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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

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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 🖕

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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

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🚀 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 ⭐️

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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 ✉️

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