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 68 146 subscribers, ranking 2 377 in the Education category and 4 752 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 146 subscribers.
According to the latest data from 02 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 69 over the last 30 days and by 4 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.76%. Within the first 24 hours after publication, content typically collects 1.55% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 565 views. Within the first day, a publication typically gains 1 054 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 03 September, 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.
68 146
Subscribers
+424 hours
+427 days
+6930 days
Posts Archive
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📊 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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📚 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
https://t.me/CodeProgrammer ✅
👩💻 A simple explanation of working with list in Python!
https://t.me/CodeProgrammer ✅
👩💻 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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Repost from Python Courses & Resources
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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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