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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
Ko'proq ko'rsatish📈 Telegram kanali Machine Learning with Python analitikasi
Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 67 829 obunachidan iborat bo'lib, Taʼlim toifasida 2 404-o'rinni va Hindiston mintaqasida 5 049-o'rinni egallagan.
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05 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 77 ga, so‘nggi 24 soatda esa 9 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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- Tematik yo‘nalishlar: Kontent insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
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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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 06 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
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Obunachilar
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Postlar arxiv
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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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It includes full-fledged lectures at a top-university level, available for free.
What's in the course
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- want a systematic understanding instead of individual tutorials
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https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/video_galleries/lecture-videos/
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