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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📈 Аналитический обзор Telegram-канала Machine Learning with Python
Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 475 подписчиков, занимая 2 403 место в категории Образование и 4 579 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 475 подписчиков.
Согласно последним данным от 03 ноября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -74, а за последние 24 часа — 12, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 4.07%. В первые 24 часа после публикации контент обычно набирает 1.55% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 789 просмотров. В течение первых суток публикация набирает 1 062 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 8.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как insidead, learning, degree, evaluation, algorithm.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Благодаря высокой частоте обновлений (последние данные получены 04 ноября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
68 475
Подписчики
+1224 часа
-827 дней
-7430 дней
Архив постов
📊 Data Visualization Learning Roadmap
👩🏻💻 Many people ask me where to start to learn "data visualization"?
✏️ The truth is that data visualization has its own language that we need to learn. We need to start with the basics and then gradually move on to more complex topics.
✅ I have used some great resources along the way that I am still learning from. With every project, consultation, or course I take, my experiences grow. Here is a summary of the useful resources that have helped me:👇
1️⃣ Part One: Articles and Books:
📑 Full article by Lisa Charlotte Muth
🔗 Article link: started data visualization
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2️⃣ Books:
📚 Book: Storytelling with Data
📚 Book: Practical Charts
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3️⃣ Learning communities:
👤 Membership link: Storytelling with Data
☑️ Membership link: DataVis Community
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4️⃣ Podcasts:
🌟 Link: SWD Podcast
🌟 Link: Data Viz Today
🌟 Link: PolicyViz
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5️⃣ Key resources:
📚 Data Visualization Guide: This guide includes great resources for learning and developing data visualization skills.
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6️⃣ Training courses:
📣 Effective data visualization / Learning to turn data into art.
📣 Information design /storytelling with data in Illustrator.
📣 Data Visualization / This course provides creative tools and techniques for visualization.
➖➖➖➖➖➖➖➖➖
7️⃣ Data visualization tools:
⬅️ Link: Scimago Graphica
⬅️ Link: Datawrapper
⬅️ Link: Flourish
⬅️ Link: Tableau Public
⬅️ Link: RStudio & Python
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👩💻 A simple explanation of working with list in Python!
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👩💻 algorithms is a useful repository with a collection of algorithms implemented in Python!
🌟 It covers a wide range of algorithmic topics, including sorting, searching, graph manipulation, data structures, dynamic programming, cryptography, and more. The main goal of the repository is to provide an educational resource for learning algorithms and improving programming skills.
🔐 License: MIT
🖥 Github
https://t.me/CodeProgrammer ✅
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👨🏻💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars.
✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more.
✅ Why should we use it?
🔢 For learning: If you're looking to learn algorithms in action, this is great.
🔢 For practice: You can take the codes, run them, and modify them to better understand.
🔢 For projects : You can even use the codes here in real-life or academic projects.
🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms.
┌ 🏳️🌈 The Algorithms - Python
└ 🐱 GitHub-Repos
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