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Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

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Everything about programming for beginners * Python programming * Java programming * App development * Machine Learning * Data Science Managed by: @love_data

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📈 تحلیل کانال تلگرام Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

کانال Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books (@programming_guide) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 56 086 مشترک است و جایگاه 2 372 را در دسته فناوری و برنامه‌ها و رتبه 6 617 را در منطقه الهند دارد.

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از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 56 086 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 04 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 134 و در ۲۴ ساعت گذشته برابر 5 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 2.35% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 0.88% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 1 317 بازدید دریافت می‌کند. در اولین روز معمولاً 494 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 3 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند algorithm, structure, stack, javascript, programming تمرکز دارد.

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Everything about programming for beginners * Python programming * Java programming * App development * Machine Learning * Data Science Managed by: @love_data

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 05 ژوئن, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامه‌ها تبدیل کرده‌اند.

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If you are starting a new project, you must first choose a tech stack. Project starter pack: - Backend: .NET - Mobile: .NET MAUI(or AvaloniaUI or Uno) - Frontend: Blazor or Angular - Database: SQL Server or PostgreSQL - Testing: xUnit + NSubstitute Depending on the project type, mix and match various technologies. This list has evolved over time because the key is to have a tech stack that allows you to build a robust and maintainable product. Not just to follow trends. Web Development Best Resources ∟📂 topmate.io/coding/930165 ENJOY LEARNING 👍👍

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🔰 How to become a data scientist in 2025? 👨🏻‍💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field. 🔢 Step 1: Strengthen your math and statistics! ✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master: ✅ Linear algebra: matrices, vectors, eigenvalues. 🔗 Course: MIT 18.06 Linear AlgebraCalculus: derivative, integral, optimization. 🔗 Course: MIT Single Variable CalculusStatistics and probability: Bayes' theorem, hypothesis testing. 🔗 Course: Statistics 110 ➖➖➖➖➖ 🔢 Step 2: Learn to code. ✏️ Learn Python and become proficient in coding. The most important topics you need to master are: ✅ Python: Pandas, NumPy, Matplotlib libraries 🔗 Course: FreeCodeCamp Python CourseSQL language: Join commands, Window functions, query optimization. 🔗 Course: Stanford SQL CourseData structures and algorithms: arrays, linked lists, trees. 🔗 Course: MIT Introduction to Algorithms ➖➖➖➖➖ 🔢 Step 3: Clean and visualize data ✏️ Learn how to process and clean data and then create an engaging story from it! ✅ Data cleaning: Working with missing values ​​and detecting outliers. 🔗 Course: Data CleaningData visualization: Matplotlib, Seaborn, Tableau 🔗 Course: Data Visualization Tutorial ➖➖➖➖➖ 🔢 Step 4: Learn Machine Learning ✏️ It's time to enter the exciting world of machine learning! You should know these topics: ✅ Supervised learning: regression, classification. ✅ Unsupervised learning: clustering, PCA, anomaly detection. ✅ Deep learning: neural networks, CNN, RNN 🔗 Course: CS229: Machine Learning ➖➖➖➖➖ 🔢 Step 5: Working with Big Data and Cloud Technologies ✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing. ✅ Big Data Tools: Hadoop, Spark, Dask ✅ Cloud platforms: AWS, GCP, Azure 🔗 Course: Data Engineering ➖➖➖➖➖ 🔢 Step 6: Do real projects! ✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio. ✅ Kaggle competitions: solving real-world challenges. ✅ End-to-End projects: data collection, modeling, implementation. ✅ GitHub: Publish your projects on GitHub. 🔗 Platform: Kaggle🔗 Platform: ods.ai ➖➖➖➖➖ 🔢 Step 7: Learn MLOps and deploy models ✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model. ✅ MLOps training: model versioning, monitoring, model retraining. ✅ Deployment models: Flask, FastAPI, Docker 🔗 Course: Stanford MLOps Course ➖➖➖➖➖ 🔢 Step 8: Stay up to date and network ✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field. Read scientific articles: arXiv, Google Scholar ✅ Connect with the data community: 🔗 Site: Papers with code 🔗 Site: AI Research at Google
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