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Red:📈 Análisis del canal de Telegram Machine Learning & Artificial Intelligence | Data Science Free Courses
El canal Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 228 suscriptores, ocupando la posición 2 421 en la categoría Educación y el puesto 428 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 228 suscriptores.
Según los últimos datos del 15 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 575, y en las últimas 24 horas de 44, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.57%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.32% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 053 visualizaciones. En el primer día suele acumular 890 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 6.
- Intereses temáticos: El contenido se centra en temas clave como sellerflash, waybienad, pricing, buybox, buyer.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 16 julio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
67 228
Suscriptores
+4424 horas
+1587 días
+57530 días
Carga de datos en curso...
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| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 16 julio | +8 | |||
| 15 julio | +44 | |||
| 14 julio | +8 | |||
| 13 julio | +28 | |||
| 12 julio | +23 | |||
| 11 julio | +24 | |||
| 10 julio | +18 | |||
| 09 julio | +45 | |||
| 08 julio | +32 | |||
| 07 julio | +13 | |||
| 06 julio | +22 | |||
| 05 julio | +29 | |||
| 04 julio | +35 | |||
| 03 julio | +44 | |||
| 02 julio | +52 | |||
| 01 julio | +41 |
Publicaciones del Canal
✅ 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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