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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Network:📈 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 68 102 subscribers, ranking 2 372 in the Education category and 4 808 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 102 subscribers.
According to the latest data from 27 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 112 over the last 30 days and by 8 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.52%. Within the first 24 hours after publication, content typically collects 1.90% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 077 views. Within the first day, a publication typically gains 1 291 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
- 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 28 August, 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.
68 102
Subscribers
+824 hours
-727 days
+11230 days
Posts Archive
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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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
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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
https://t.me/CodeProgrammer ✅
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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
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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.
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.
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https://t.me/CodeProgrammer
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