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Machine Learning & Artificial Intelligence | Data Science Free Courses

Machine Learning & Artificial Intelligence | Data Science Free Courses

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📈 نظرة تحليلية على قناة تيليجرام Machine Learning & Artificial Intelligence | Data Science Free Courses

تُعد قناة Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 67 228 مشتركاً، محتلاً المرتبة 2 421 في فئة التعليم والمرتبة 428 في منطقة ماليزيا.

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

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منشورات القناة
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 Micr
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 U
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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👑 Types of Machine Learning
👑 Types of Machine Learning
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