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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📈 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 68 475 підписників, посідаючи 2 403 місце в категорії Освіта та 4 579 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 68 475 підписників.
За останніми даними від 03 листопада, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -74, а за останні 24 години на 12, 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 1.55% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 789 переглядів. Протягом першої доби публікація в середньому набирає 1 062 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 8.
- 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 04 листопада, 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.
68 475
Підписники
+1224 години
-827 днів
-7430 днів
Архів дописів
📊 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:👇
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📑 Full article by Lisa Charlotte Muth
🔗 Article link: started data visualization
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2️⃣ Books:
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📚 Book: Practical Charts
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3️⃣ Learning communities:
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☑️ Membership link: DataVis Community
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🌟 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
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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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