Machine Learning & Artificial Intelligence | Data Science Free Courses
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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
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Network:📈 Analytical overview of Telegram channel Machine Learning & Artificial Intelligence | Data Science Free Courses
Channel Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) in the English language segment is an active participant. Currently, the community unites 67 228 subscribers, ranking 2 421 in the Education category and 428 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 228 subscribers.
According to the latest data from 15 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 575 over the last 30 days and by 44 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 1.57%. Within the first 24 hours after publication, content typically collects 1.32% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 053 views. Within the first day, a publication typically gains 890 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 6.
- Thematic interests: Content is focused on key topics such as sellerflash, waybienad, pricing, buybox, buyer.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
Thanks to the high frequency of updates (latest data received on 16 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
67 228
Subscribers
+4424 hours
+1587 days
+57530 days
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| Date | Subscriber Growth | Mentions | Channels | |
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| 01 July | +41 |
Channel Posts
✅ 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| 2 | ✅ 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! | 2 080 |
| 3 |  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. | 1 879 |
| 4 |  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 | 2 041 |
| 5 | 🔟 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 👍👍 | 3 218 |
| 6 | 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 | 3 901 |
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| 13 |  👑 Types of Machine Learning | 5 813 |
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