Machine Learning & Artificial Intelligence | Data Science Free Courses

前往频道在 Telegram

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

显示更多

📈 Telegram 频道 Machine Learning & Artificial Intelligence | Data Science Free Courses 的分析概览

频道 Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) 英语语言赛道中的是活跃参与者。目前社区聚集了 67 183 名订阅者，在教育类别中位列第 2 427，并在 马来西亚 地区排名第 429 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 67 183 名订阅者。

根据 13 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 606，过去 24 小时变化为 28，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 1.13%。内容发布后 24 小时内通常能获得 1.32% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 758 次浏览，首日通常累积 887 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 5。
主题关注点： 内容集中在 sellerflash, waybienad, pricing, buybox, buyer 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun”

凭借高频更新（最新数据采集于 14 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为教育类别中的关键影响点。

67 183

订阅者

+2824 小时

+1647 天

+60630 天

758

帖子浏览量

~ 88724 小时

~ 1 19448 小时

1.13%

参与率

~ 1

每日帖子数

Ads index

beta

帖子存档

67 183

✅ 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

67 183

✅ 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!

67 183

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.

67 183

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

67 183

🔟 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 👍👍

67 183

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

67 183