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 68 161 subscribers, ranking 2 376 in the Education category and 4 740 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 161 subscribers.
According to the latest data from 05 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 51 over the last 30 days and by -1 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.53%. Within the first 24 hours after publication, content typically collects 1.57% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 728 views. Within the first day, a publication typically gains 1 071 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 06 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.
68 161
Subscribers
-124 hours
+577 days
+5130 days
Posts Archive
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🔺 Data science learning roadmap in 2024
👨🏻💻 If you want to start learning data science from scratch in 2024, this roadmap can be a great starting point for you.👌🏼
1️⃣ Basics of data science
🏷 Statistics and probability
◾┘️ link: Statistics & Probability
🏷 Linear Algebra
┘ ◽️ link: Essence of Linear Algebra
2️⃣ Programming language
🏷 Python
◾┘️ link: Learn Python 3
3️⃣ Data analysis and manipulation
🏷 Pandas library
◾┘️ link: pandas documentation
🏷 Data preparation with Pandas
Link : Data Wrangling with Pandas
🏷 NumPy library
◾┘️ link: NumPy documentation
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🏷 Matplotlib and Seaborn library
◾┘️ Link: Matplotlib / seaborn
🏷 Tableau Public platform
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5️⃣ Principles of machine learning
🏷 scikit-learn library
◾┘️ link: scikit-learn
6️⃣ Learning algorithms
🏷 Hands-On Machine Learning book
◾┘️ link: Hands-On ML
7️⃣ Deep learning
🏷 TensorFlow library
◾┘️ link: TensorFlow Tutorial
🏷 PyTorch library
◾┘️ Link: PyTorch Documentation
8️⃣ Big data technologies
🏷 Spark framework course
◾┘️ Link: Spark Course
9️⃣ Advanced topics
🏷 Natural language processing course in Python
◾┘️ link: NLP in Python
1️⃣ Share your projects on Kaggle and GitHub
🏷 Kaggle platform
◾┘️ link: Kaggle
🏷 GitHub platform
◾┘️ Link: GitHub
🔺 Data science learning roadmap in 2024
✅ If you want to start learning data science from scratch in 2024, this roadmap can be a great starting point for you.
1️⃣ Basics of data science
🏷 Statistics and probability
◾️┘️ link: Statistics & Probability
🏷 Linear Algebra
◾️┘️ link: Essence of Linear Algebra
2️⃣ Programming language
🏷 Python
◾️┘️ link: Learn Python 3
3️⃣ Data analysis and manipulation
🏷 Pandas library
◾️┘️ link: pandas documentation
🏷 Data preparation with Pandas
◾️┘️ Link : Data Wrangling with Pandas
🏷 NumPy library
◾️┘️ link: NumPy documentation
4️⃣ Data visualization
🏷 Matplotlib and Seaborn library
◾️┘️ Link: Matplotlib / seaborn
🏷 Tableau Public platform
◾️┘️ Link : Tableau Public
5️⃣ Principles of machine learning
🏷 scikit-learn library
◾️┘️ link: scikit-learn
6️⃣ Learning algorithms
🏷 Hands-On Machine Learning book
◾️┘️ link: Hands-On ML
7️⃣ Deep learning
🏷 TensorFlow library
◾️┘️ link: TensorFlow Tutorial
🏷 PyTorch library
◾️┘️ Link: PyTorch Documentation
8️⃣ Big data technologies
🏷 Spark framework course
◾️┘️ Link: Spark Course
9️⃣ Advanced topics
🏷 Natural language processing course in Python
◾️┘️ link: NLP in Python
1️⃣ Share your projects on Kaggle and GitHub
🏷 Kaggle platform
◾️┘️ link: Kaggle
🏷 GitHub platform
◾️┘️ Link: GitHub
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┤ ◻️ Supply chain analysis course
┤ ◼️ Understanding the world through data course
┤ ◻️ Computational thinking course for modeling and simulation
┤ ◼️ Probability Course - Science of Uncertainty and Data
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┤ ◼️ Principles and basics of statistics and probability course
┘ ◻️ The course of becoming an entrepreneur
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