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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Сітка:📈 Аналітичний огляд Telegram-каналу Machine Learning with Python
Канал Machine Learning with Python (@codeprogrammer) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 68 137 підписників, посідаючи 2 366 місце в категорії Освіта та 4 740 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 68 137 підписників.
За останніми даними від 30 серпня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 86, а за останні 24 години на 27, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 4.07%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.52% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 775 переглядів. Протягом першої доби публікація в середньому набирає 1 037 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як insidead, learning, degree, evaluation, algorithm.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Завдяки високій частоті оновлень (останні дані отримано 31 серпня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
68 137
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+2724 години
-297 днів
+8630 день
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📺 12 comprehensive playlists to master
⬅️ machine learning, deep learning, and GenAI!
👨🏻💻 Each playlist is designed to be simple and understandable for beginners, and then gradually dive deeper into the topics.
😉 Machine Learning Basics (39 videos)
😉 Python for ML (9 videos)
😉 Optimization for ML (5 videos)
😉 Machine Learning with Practical Exercises (37 videos)
😉 Building Decision Trees from Scratch (13 videos)
😉 Building Neural Networks from Scratch (35 videos)
😉 Graph Neural Networks (6 videos)
😉 Computer Vision from Scratch (19 videos)
😉 Building LLM from Scratch (43 videos)
😉 Reasoning in LLMs from Scratch (22 videos)
😉 Building DeepSeek from Scratch (29 videos)
😉 Machine Learning in Production Environment (6 videos)
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👨🏻💻 This Python library helps you extract usable data for language models from complex files like tables, images, charts, or multi-page documents.
📝 The idea of Agentic Document Extraction is that unlike common methods like OCR that only read text, it can also understand the structure and relationships between different parts of the document. For example, it understands which title belongs to which table or image.
✅ Works with PDFs, images, and website links.
☑️ Can chunk and process very large documents (up to 1000 pages) by itself.
✔️ Outputs both JSON and Markdown formats.
☑️ Even specifies the exact location of each section on the page.
✔️ Supports parallel and batch processing.
pip install agentic-doc
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Repost from Machine Learning with Python
“Learn AI” is everywhere. But where do the builders actually start?
Here’s the real path, the courses, papers and repos that matter.
✅ Videos:
Everything here ⇒ https://lnkd.in/ePfB8_rk
➡️ LLM Introduction → https://lnkd.in/ernZFpvB
➡️ LLMs from Scratch - Stanford CS229 → https://lnkd.in/etUh6_mn
➡️ Agentic AI Overview →https://lnkd.in/ecpmzAyq
➡️ Building and Evaluating Agents → https://lnkd.in/e5KFeZGW
➡️ Building Effective Agents → https://lnkd.in/eqxvBg79
➡️ Building Agents with MCP → https://lnkd.in/eZd2ym2K
➡️ Building an Agent from Scratch → https://lnkd.in/eiZahJGn
✅ Courses:
All Courses here ⇒ https://lnkd.in/eKKs9ves
➡️ HuggingFace's Agent Course → https://lnkd.in/e7dUTYuE
➡️ MCP with Anthropic → https://lnkd.in/eMEnkCPP
➡️ Building Vector DB with Pinecone → https://lnkd.in/eP2tMGVs
➡️ Vector DB from Embeddings to Apps → https://lnkd.in/eP2tMGVs
➡️ Agent Memory → https://lnkd.in/egC8h9_Z
➡️ Building and Evaluating RAG apps → https://lnkd.in/ewy3sApa
➡️ Building Browser Agents → https://lnkd.in/ewy3sApa
➡️ LLMOps → https://lnkd.in/ex4xnE8t
➡️ Evaluating AI Agents → https://lnkd.in/eBkTNTGW
➡️ Computer Use with Anthropic → https://lnkd.in/ebHUc-ZU
➡️ Multi-Agent Use → https://lnkd.in/e4f4HtkR
➡️ Improving LLM Accuracy → https://lnkd.in/eVUXGT4M
➡️ Agent Design Patterns → https://lnkd.in/euhUq3W9
➡️ Multi Agent Systems → https://lnkd.in/evBnavk9
✅ Guides:
Access all ⇒ https://lnkd.in/e-GA-HRh
➡️ Google's Agent → https://lnkd.in/encAzwKf
➡️ Google's Agent Companion → https://lnkd.in/e3-XtYKg
➡️ Building Effective Agents by Anthropic → https://lnkd.in/egifJ_wJ
➡️ Claude Code Best practices → https://lnkd.in/eJnqfQju
➡️ OpenAI's Practical Guide to Building Agents → https://lnkd.in/e-GA-HRh
✅ Repos:
➡️ GenAI Agents → https://lnkd.in/eAscvs_i
➡️ Microsoft's AI Agents for Beginners → https://lnkd.in/d59MVgic
➡️ Prompt Engineering Guide → https://lnkd.in/ewsbFwrP
➡️ AI Agent Papers → https://lnkd.in/esMHrxJX
✅ Papers:
🟡 ReAct → https://lnkd.in/eZ-Z-WFb
🟡 Generative Agents → https://lnkd.in/eDAeSEAq
🟡 Toolformer → https://lnkd.in/e_Vcz5K9
🟡 Chain-of-Thought Prompting → https://lnkd.in/eRCT_Xwq
🟡 Tree of Thoughts → https://lnkd.in/eiadYm8S
🟡 Reflexion → https://lnkd.in/eggND2rZ
🟡 Retrieval-Augmented Generation Survey → https://lnkd.in/eARbqdYE
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Python Cheat Sheet (very very important)
📖 Compact Python cheat sheet covering setup, syntax, data types, variables, strings, control flow, functions, classes, errors, and I/O.
Link: https://discord.com/channels/942740928706281524/1423994784720359567/1424711790947864669
Big surprise in our channels on Discord
https://discord.gg/PGZku7DrSz
Repost from Machine Learning
📌 Missing Value Imputation, Explained: A Visual Guide with Code Examples for Beginners
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-08-27 | ⏱️ Read time: 13 min read
One (tiny) dataset, six imputation methods?
Repost from Data Analytics
🖥 Extremely useful collection of 800+ SQL questions frequently asked in interviews.
It also includes tasks for self-study and many examples.
The collection is perfect for those who want to improve their SQL skills, refresh their knowledge, and test themselves.
▪️ GitHub
https://t.me/addlist/8_rRW2scgfRhOTc0 ⚡️
Great find for developers: free cheat sheets on Deep Learning and PyTorch
A detailed guide to creating and training neural networks - link
Basic principles and practice of working with PyTorch - link
👉 @CODEPROGRAMMER
Awesome interactive textbook on probability theory and statistics
Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works
Get it here, I recommend opening it on a desktop
https://seeing-theory.brown.edu/
👉 @DataScienceM
Repost from Machine Learning
📌 Extracting Structured Vehicle Data from Images
🗂 Category:
🕒 Date: 2025-01-27 | ⏱️ Read time: 10 min read
Build an Automated Vehicle Documentation System that Extracts Structured Information from Images, using OpenAI API,…
Awesome interactive textbook on probability theory and statistics
Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works
Get it here, I recommend opening it on a desktop
https://seeing-theory.brown.edu/
👉 @DataScienceM
Repost from Machine Learning
Awesome interactive textbook on probability theory and statistics
Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works
Get it here, I recommend opening it on a desktop
https://seeing-theory.brown.edu/
👉 @DataScienceM
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Repost from Machine Learning
📌 How to Build a Genetic Algorithm from Scratch in Python
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-30 | ⏱️ Read time: 16 min read
A complete walkthrough on how one can build a Genetic Algorithm from scratch in Python,…
Repost from Github Top Repositories
Python library RetinaFace for face detection and working with key points (eyes, nose, mouth)
Supports face alignment, easily installed via
pip install retina-face, and works based on deep models from the insightface project.
An excellent tool for tasks in computer vision and face recognition.
Usage examples:
from retinaface import RetinaFace
resp = RetinaFace.detect_faces("img1.jpg")
print(resp)
{
"face_1": {
"score": 0.9993440508842468,
"facial_area": [155, 81, 434, 443],
"landmarks": {
"right_eye": [257.82974, 209.64787],
"left_eye": [374.93427, 251.78687],
"nose": [303.4773, 299.91144],
"mouth_right": [228.37329, 338.73193],
"mouth_left": [320.21982, 374.58798]
}
}
}