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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
Show more📈 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 67 819 subscribers, ranking 2 404 in the Education category and 5 049 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 819 subscribers.
According to the latest data from 05 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 77 over the last 30 days and by 9 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.60%. Within the first 24 hours after publication, content typically collects 2.50% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 767 views. Within the first day, a publication typically gains 1 695 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 6.
- 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 June, 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.
67 819
Subscribers
+924 hours
+587 days
+7730 days
Posts Archive
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Pandas cheat sheet
Use the following Pandas cheat sheet to quickly reference some of the most common operations you might perform with the Pandas library.
More: https://www.coursera.org/resources/pandas-cheat-sheet
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Repost from Learn Python Coding
Python Cheat Sheet: Beginner to Expert Guide
This #Python cheat sheet covers basics to advanced concepts, regex, list slicing, loops and more. Perfect for quick reference and enhancing your coding skills.
Read: https://www.almabetter.com/bytes/cheat-sheet/python
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Matplotlib Cheat Sheet (Basics to Advanced)
Learn key Matplotlib functions with our Matplotlib cheat sheet. Includes examples, advanced customizations and comparison with Seaborn for better visualizations
Matplotlib is a versatile library in Python used for data visualization. Matplotlib enables the creation of static, interactive, and animated visualizations in Python. It is highly customizable and integrates well with libraries like Pandas and NumPy. Its pyplot module simplifies the process of creating plots similar to MATLAB. This Matplotlib cheat sheet provides an overview of the essential functions, features, and tools available in Matplotlib, along with comparisons to Seaborn where relevant.
Read: https://www.almabetter.com/bytes/cheat-sheet/matplotlib
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This cheat sheet—part of our Complete Guide to NumPy, pandas, and Data Visualization—offers a handy reference for essential pandas commands, focused on efficient data manipulation and analysis. Using examples from the Fortune 500 Companies Dataset, it covers key pandas operations such as reading and writing data, selecting and filtering DataFrame values, and performing common transformations.
You'll find easy-to-follow examples for grouping, sorting, and aggregating data, as well as calculating statistics like mean, correlation, and summary statistics. Whether you're cleaning datasets, analyzing trends, or visualizing data, this cheat sheet provides concise instructions to help you navigate pandas’ powerful functionality.
Designed to be practical and actionable, this guide ensures you can quickly apply pandas’ versatile data manipulation tools in your workflow.
Sometimes the timing is more important than the prediction. The chart will always go up slowly, you need to exit before it crashes.
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Repost from Machine Learning
10 GitHub Repositories to Master System Design
Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.
Most engineers encounter system design when preparing for interviews, but in reality, it is much bigger than that. System design is about understanding how large-scale systems are built, why certain architectural decisions are made, and how trade-offs shape everything from performance to reliability. Behind every app you use daily, from messaging platforms to streaming services, there are careful decisions about databases, caching, load balancing, fault tolerance, and consistency models. What makes system design challenging is that there is rarely a single correct answer. You are constantly balancing cost, scalability, latency, complexity, and future growth. Should you shard the database now or later? Do you prioritize strong consistency or eventual consistency? Do you optimize for reads or writes? These are the kinds of questions that separate surface-level knowledge from real architectural thinking. The good news is that many experienced engineers have documented these patterns, breakdowns, and interview strategies openly on GitHub. Instead of learning only through trial and error, you can study real case studies, curated resources, structured interview frameworks, and production-grade design principles from the community. In this article, we review 10 GitHub repositories that cover fundamentals, interview preparation, distributed systems concepts, machine learning system design, agent-based architectures, and real-world scalability case studies. Together, they provide a practical roadmap for developing the structured thinking required to design reliable systems at scale. Read: https://www.kdnuggets.com/10-github-repositories-to-master-system-design https://t.me/DataScienceM ✅
Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory
Need help choosing the right Python dataframe library? This article compares Pandas and Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
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