Machine Learning & Artificial Intelligence | Data Science Free Courses

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الشبكة:Data Analytics ماليزيا429 التعليم2 427...

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تُعد قناة Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 67 183 مشتركاً، محتلاً المرتبة 2 427 في فئة التعليم والمرتبة 429 في منطقة ماليزيا.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 67 183 مشتركاً.

بحسب آخر البيانات بتاريخ 13 يوليو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 606، وفي آخر 24 ساعة بمقدار 28، مع بقاء الوصول العام مرتفعاً.

حالة التحقق: غير موثّقة
معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 1.13‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.32‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
وصول المنشورات: يحصل كل منشور على متوسط 758 مشاهدة. وخلال اليوم الأول يجمع عادةً 887 مشاهدة.
التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 5.
الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل sellerflash, waybienad, pricing, buybox, buyer.

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“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun”

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 14 يوليو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.

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المشتركون

+2824 ساعات

+1647 أيام

+60630 أيام

758

عرض المشاهدات

~ 88724 ساعات

~ 1 19448 ساعات

1.13%

معدل المشاركة

~ 1

المشاركات في اليوم

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beta

أرشيف المشاركات

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✅ Statistics & Probability Cheatsheet 📚🧠 📌 Descriptive Statistics: ⦁ Mean = (Σx) / n ⦁ Median = Middle value ⦁ Mode = Most frequent value ⦁ Variance (σ²) = Σ(x - μ)² / n ⦁ Std Dev (σ) = √Variance ⦁ Range = Max - Min ⦁ IQR = Q3 - Q1 📌 Probability Basics: ⦁ P(A) = Outcomes A / Total Outcomes ⦁ P(A ∩ B) = P(A) × P(B) (if independent) ⦁ P(A ∪ B) = P(A) + P(B) - P(A ∩ B) ⦁ Conditional: P(A|B) = P(A ∩ B) / P(B) ⦁ Bayes’ Theorem: P(A|B) = [P(B|A) × P(A)] / P(B) 📌 Common Distributions: ⦁ Binomial (fixed trials) ⦁ Normal (bell curve) ⦁ Poisson (rare events over time) ⦁ Uniform (equal probability) 📌 Inferential Stats: ⦁ Z-score = (x - μ) / σ ⦁ Central Limit Theorem: sampling dist ≈ Normal ⦁ Confidence Interval: CI = x‌ ± z*(σ/√n) 📌 Hypothesis Testing: ⦁ H₀ = No effect; H₁ = Effect present ⦁ p-value < α → Reject H₀ ⦁ Tests: t-test (small samples), z-test (known σ), chi-square (categorical data) 📌 Correlation: ⦁ Pearson: linear relation (–1 to 1) ⦁ Spearman: rank-based correlation 🧪 Tools to Practice: Python packages: scipy.stats, statsmodels, pandas Visualization: seaborn, matplotlib 💡 Quick tip: Use these formulas to crush interviews and build solid ML foundations! 💬 Tap ❤️ for more

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✅ If you're serious about learning Python for data science, automation, or interviews — just follow this roadmap 🐍💻 1. Install Python Jupyter Notebook (via Anaconda or VS Code) 2. Learn print(), variables, and data types 📦 3. Understand lists, tuples, sets, and dictionaries 🔁 4. Master conditional statements (if, elif, else) ✅❌ 5. Learn loops (for, while) 🔄 6. Functions – defining and calling functions 🔧 7. Exception handling – try, except, finally ⚠️ 8. String manipulations formatting ✂️ 9. List dictionary comprehensions ⚡ 10. File handling (read, write, append) 📁 11. Python modules packages 📦 12. OOP (Classes, Objects, Inheritance, Polymorphism) 🧱 13. Lambda, map, filter, reduce 🔍 14. Decorators Generators ⚙️ 15. Virtual environments pip installs 🌐 16. Automate small tasks using Python (emails, renaming, scraping) 🤖 17. Basic data analysis using Pandas NumPy 📊 18. Explore Matplotlib Seaborn for visualization 📈 19. Solve Python coding problems on LeetCode/HackerRank 🧠 20. Watch a mini Python project (YouTube) and build it step by step 🧰 21. Pick a domain (web dev, data science, automation) and go deep 🔍 22. Document everything on GitHub 📁 23. Add 1–2 real projects to your resume 💼 Trick: Copy each topic above, search it on YouTube, watch a 10-15 min video, then code along. 🎯 This method builds actual understanding + project experience for interviews! 💬 Tap ❤️ for more!

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𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀🎓 Offers a wide range of free learning resources through Microsoft Learn, helping students, freshers, and professionals build job-ready skills at their own pace. ✅ 100% FREE self-paced learning modules ✅ Official learning platform from Microsoft 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/4paqRJS Explore Microsoft’s free resources. Build in-demand skills and make your profile stronger.

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GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model

The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.

What’s inside: 🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale; 🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer; 🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features; 🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load; 🔘Two MTP heads, enabling up to 2.2x faster generation; 🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels; 🔘A new online RL stage after SFT and DPO. Results: 🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks: 🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size; 🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.

The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.

➡️ HuggingFace

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🔟 Free useful resources to learn Machine Learning 👉 Google https://developers.google.com/machine-learning/crash-course 👉 Leetcode https://leetcode.com/explore/featured/card/machine-learning-101 👉 Hackerrank https://www.hackerrank.com/domains/ai/machine-learning 👉 Hands-on Machine Learning https://t.me/datasciencefun/424 👉 FreeCodeCamp https://www.freecodecamp.org/learn/machine-learning-with-python/ 👉 Machine learning projects https://t.me/datasciencefun/392 👉 Kaggle https://www.kaggle.com/learn/intro-to-machine-learning https://www.kaggle.com/learn/intermediate-machine-learning 👉 Geeksforgeeks https://www.geeksforgeeks.org/machine-learning/ 👉 Create ML Models https://docs.microsoft.com/en-us/learn/paths/create-machine-learn-models/ 👉 Machine Learning Test Cheat Sheet https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/ Join @free4unow_backup for more free resources ENJOY LEARNING 👍👍

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5 YouTubers who teach AI better than any paid courses 👇 1/ Andrej Karpathy: youtube.com/@AndrejKarpathy 2/ 3Blue1Brown — youtube.com/@3blue1brown 3/ Sentdex — youtube.com/@sentdex 4/ Yannic Kilcher — youtube.com/@YannicKilcher 5/ Tina Huang — youtube.com/@TinaHuang1 React to this ❤️ for more such content

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