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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
Показати більше📈 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 67 813 підписників, посідаючи 2 416 місце в категорії Освіта та 5 038 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 67 813 підписників.
За останніми даними від 09 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 70, а за останні 24 години на 10, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 2.94%. Протягом перших 24 годин після публікації контент зазвичай збирає 2.44% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 1 997 переглядів. Протягом першої доби публікація в середньому набирає 1 652 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 7.
- 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 10 червня, 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.
67 813
Підписники
+1024 години
+127 днів
+7030 день
Архів дописів
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
Self-attention in LLMs, clearly explained
#SelfAttention #LLMs #Transformers #NLP #DeepLearning #MachineLearning #AIExplained #AttentionMechanism #AIConcepts #AIEducation
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Supervised Learning: Classification and Regression
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
#SupervisedLearning #MachineLearning #Classification #Regression #MLNotes #DataScience #AIResources #MLTheory #MLLectures #LearnML
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Anyone trying to deeply understand Large Language Models.
Checkout
Foundations of Large Language Modelsby Tong Xiao & Jingbo Zhu. It’s one of the clearest, most comprehensive resource. ⭐️ Paper Link: arxiv.org/pdf/2501.09223
#LLMs #LargeLanguageModels #AIResearch #DeepLearning #MachineLearning #AIResources #NLP #AITheory #FoundationModels #AIUnderstanding
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📖 A comprehensive cheat sheet for working with Polars
🌟 Have you ever worked with pandas and thought that was the fastest way? I thought the same thing until I worked with Polars.
✏️ This cheat sheet explains everything about Polars in a concise and simple way. Not just theory! But also a bunch of real examples, practical experience, and projects that will really help you in the real world.
┌ 🐻❄️ Polars Cheat Sheet
├ ♾️ Google Colab
└ 📖 Doc
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👫 Preparing for Data Science Interviews
👨🏻💻 I've been collecting a variety of data science interview questions for different positions for a few weeks now.
✅ I covered everything, from basic to advanced:
Common Data Science and ML Questions (34 questions)
Regression (22 questions)
Classification (39 questions)
SVM algorithms, decision tree
Simple Bayes and statistical discussions and...🚨 This list is regularly updated and categorized so that you can easily prepare for the interview step by step.👇 ┌📝 Interview Questions └🐱 GitHub-Repos
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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
🔥 How to become a data scientist in 2025?
1️⃣ First of all, strengthen your foundation (math and statistics) .
✏️ If you don't know math, you'll run into trouble wherever you go. Every model you build, every analysis you do, there's a world of math behind it. You need to know these things well:
✅ Linear Algebra: Link
✅ Calculus: Link
✅ Statistics and Probability: Link
➖➖➖➖➖➖
2️⃣ Then learn programming !
✏️ Without further ado, get started learning Python and SQL.
✅ Python: Link
✅ SQL language: Link
✅ Data Structures and Algorithms: Link
➖➖➖➖➖➖
3️⃣ Learn to clean and analyze data!
✏️ Data is always messy, and a data scientist must know how to organize it and extract insights from it.
✅ Data cleansing: Link
✅ Data visualization: Link
➖➖➖➖➖➖
4️⃣ Learn machine learning !
✏️ Once you've mastered the basic skills, it's time to enter the world of machine learning. Here's what you need to know:
◀️ Supervised learning: regression, classification
◀️ Unsupervised learning: clustering, dimensionality reduction
◀️ Deep learning: neural networks, CNN, RNN
✅ Stanford University CS229 course: Link
➖➖➖➖➖➖
5️⃣ Get to know big data and cloud computing !
✏️ Large companies are looking for people who can work with large volumes of data.
◀️ Big data tools (e.g. Hadoop, Spark, Dask)
◀️ Cloud services (AWS, GCP, Azure)
➖➖➖➖➖➖
6️⃣ Do a real project and build a portfolio !
✏️ Everything you've learned so far is worthless without a real project!
◀️ Participate in Kaggle and work with real data.
◀️ Do a project from scratch (from data collection to model deployment)
◀️ Put your code on GitHub.
✅ Open Source Data Science Projects: Link
➖➖➖➖➖➖
7️⃣ It's time to learn MLOps and model deployment!
✏️ Many people just build models but don't know how to deploy them. But companies want someone who can put the model into action!
◀️ Machine learning operationalization (monitoring, updating models)
◀️ Model deployment tools: Flask, FastAPI, Docker
✅ Stanford University MLOps Course: Link
➖➖➖➖➖➖
8️⃣ Always stay up to date and network!
✏️ Follow research articles on arXiv and Google Scholar.
✅ Papers with Code website: link
✅ AI Research at Google website: link
#DataScience #HowToBecomeADataScientist #ML2025 #Python #SQL #MachineLearning #MathForDataScience #BigData #MLOps #DeepLearning #AIResearch #DataVisualization #PortfolioProjects #CloudComputing #DSCareerPath
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Master Python with Ease!
I've just compiled a set of clean and powerful Python Cheat Sheets to help beginners and intermediates speed up their coding workflow.
Whether you're brushing up on the basics or diving into data science, these sheets will save you time and boost your productivity.
📌 Topics Covered:
Python Basics
Jupyter Notebook Tips
Importing Libraries
NumPy Essentials
Pandas Overview
Perfect for students, developers, and anyone looking to keep essential Python knowledge at their fingertips.
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🌟 Join the communities:
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 DataCamp has officially partnered with Polars**—a cutting-edge DataFrame library designed for speed and efficiency!
To mark this exciting collaboration, **DataCamp is offering free access to its brand-new course *“Introduction to Polars”* for the next 90 days. 🎉
This course is a great opportunity for learners and professionals alike to master data cleaning, transformation, and analysis with Polars' high-performance engine, lazy execution, and powerful groupby operations.
Unlock the full potential of data workflows and explore how Polars can supercharge large-scale data processing.
🔗 Start learning now:
https://www.datacamp.com/courses/introduction-to-polars
#DataScience #Polars #Python #BigData #DataEngineering #MachineLearning #DataAnalytics #OpenSource #DataCamp #FreeCourse #LearnDataScience🌟 Join the communities:
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⚡ A beautiful booklet for learning deep learning in a smooth and concise way without diving into the world of complexity.
✅ I highly recommend reading this enjoyable booklet.
#DeepLearning #AI #MachineLearning #LearnAI #DeepLearningForBeginners
🌟 Join the communities:
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
