Data science/ML/AI
Ir al canal en Telegram
Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
Mostrar más
Red:📈 Análisis del canal de Telegram Data science/ML/AI
El canal Data science/ML/AI (@datascience_bds) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 14 064 suscriptores, ocupando la posición 9 086 en la categoría Tecnologías y Aplicaciones y el puesto 28 213 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 14 064 suscriptores.
Según los últimos datos del 26 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 100, y en las últimas 24 horas de 3, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 9.04%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.08% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 271 visualizaciones. En el primer día suele acumular 293 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- Intereses temáticos: El contenido se centra en temas clave como panda, learning, row, api, ethic.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Data science and machine learning hub
Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources.
For beginners, data scientists and ML engineers
👉 https://rebrand.ly/bigdatachannels
DMCA: @disclosure_bds
Contact: @mldatasci...”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 27 octubre, 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 Tecnologías y Aplicaciones.
14 064
Suscriptores
+324 horas
+87 días
+10030 días
Archivo de publicaciones
14 064
Repost from Programming Quiz Channel
Which isolation level allows a transaction to read data another concurrent transaction has written but not yet committed?
14 064
Machine Learning Visualized
This is an interactive curriculum with animations and exercises that show how machine learning algorithms actually work. You can watch gradient descent, decision boundaries, neural networks, clustering, and more evolve step by step. It is great for building intuition instead of treating models as black boxes.
🎬 Free Interactive + Animation Course
⏰ Duration: Self-paced
🏃♂️ Self Paced
👨🏫 Created by: Daniel Sobrado / community project
🔗 Link
#MachineLearning #Interactive #Visualization #Course
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉 Join @bigdataspecialist for more 👈
14 064
5 LLM quantization techniques, clearly explained:
1. RTN: ignores them. Rounds every weight to the nearest grid level with no calibration data. Cheapest option, weakest at low bit widths.
2. GPTQ: repairs after rounding. Quantizes a layer column by column and adjusts the remaining weights to absorb the error before moving on.
3. AWQ: protects before rounding. Finds the ~1% of weight channels that matter most and scales them up so they survive quantization. Everything still ends up in plain INT4.
4. LLM. int8(): isolates at inference. Outlier dimensions run in FP16, the other 99.9% run in INT8, and the results are merged.
5. QAT: solves it during training. The model is fine-tuned with rounding baked into every forward pass, so it adapts to the damage before quantization is actually applied.
All five produce the same artifact, a model at a fraction of its trained precision. They differ only in where the outlier problem gets addressed.
The visual above nicely summarises these techniques.
#LLM
14 064
🗂 10 Websites for Finding Real-World Datasets
Finding good datasets is often harder than building the model. These websites cover almost every domain imaginable.
1. Kaggle Datasets
2. Hugging Face Datasets
3. Google Dataset Search
4. UCI Machine Learning Repository
5. OpenML
6. Our World in Data
7. World Bank Open Data
8. data.gov
9. FiveThirtyEight Data
10. AWS Registry of Open Data
You'll rarely run out of project ideas with these bookmarked.
#Datasets
14 064
🎲 What Makes Random Forest "Random"?
A Random Forest isn't just "many decision trees."
Each tree sees a different random sample of the data.
Then... At every split... It only considers a random subset of features.
So instead of producing 100 identical trees... You get 100 different opinions.
The final prediction is the majority vote (classification) or average (regression).
The randomness is exactly what makes the forest stronger.
14 064
📦 Your CSV Might Be Using Twice the Memory It Needs
Open a CSV in Pandas.
Run:
df.info()You'll often notice many text columns have the type:
objectIf a column contains repeated values like:
London London London Paris Paris Berlinconvert it to:
categoryInstead of storing the full text every time, Pandas stores each unique value once and references it internally. On large datasets, memory usage can drop dramatically.
14 064
From Zero to Data Scientist
This is a free, open-source curriculum from Microsoft's Azure Cloud Advocates team that breaks data science down into 20 digestible lessons spread across 10 weeks.
👉 Free curriculum with quizzes and assignments
👉 No prior experience needed to start
👉 It's Project-based so you're building a portfolio as you learn
👉 Created by Microsoft experts and students
👉 Has a strong discord community to back you up
Explore it here: https://github.com/microsoft/data-science-for-beginners
14 064
🧩 Why One-Hot Encoding Exists
Machine learning models don't understand words.
They understand numbers.
So how do you feed a value like:
Color = RedYou can't simply write:
Red = 1 Blue = 2 Green = 3The model might think Green > Blue > Red, even though colors have no natural order. Instead, we create separate columns:
Red 1 0 0 Blue 0 1 0 Green 0 0 1This is called One-Hot Encoding. It represents categories without introducing fake relationships.
14 064
🚨 UC Berkeley just open-sourced FreeToken.
It claims 2–4× faster local LLM inference than Ollama, and the wild part is the models it can run:
• Qwen3.6-35B on 8GB VRAM → 39.3 tok/s
• DeepSeek-V4-Flash 284B on 32GB VRAM → 22 tok/s
• GLM-5.2 753B on 96GB VRAM → 14.9 tok/s
How? These are Mixture-of-Experts models. A 35B model doesn't actually use all 35B parameters for every token.
FreeToken keeps the experts in system RAM and intelligently decides whether a missing expert should be sent to the GPU or computed on the CPU.
The good part is the best strategy depends on your exact machine. A 5090 desktop and an 8GB laptop may want completely opposite approaches.
It also checkpoints agent context, so coding agents don't repeatedly prefill thousands of unchanged tokens.
Open weights don't mean much if nobody can afford the hardware to run them. FreeToken is attacking that gap.
📄 Paper: https://arxiv.org/pdf/2608.16157
💻 Repo: https://github.com/FlashML-org/FreeToken
14 064
Repost from Programming Quiz Channel
Topic: SQL
🔍 Quick look before the question:
SELECT e.name, e.salary
FROM employees e
WHERE e.salary > (
SELECT AVG(salary)
FROM employees
WHERE department = e.department
);14 064
🐼 One Pandas Function That Can Save You From Ugly
if/else
Suppose you want to classify customers:
spending >= 1000 → VIP spending >= 500 → Regular otherwise → LowYou could write a complicated function. Or:
import numpy as np
df["segment"] = np.select(
[
df["spending"] >= 1000,
df["spending"] >= 500
],
[
"VIP",
"Regular"
],
default="Low"
)14 064
📢 Advertising in this channel
You can place an ad via Telega․io. It takes just a few minutes.
Formats and current rates: View details
14 064
📚 12 Free Statistics Resources Worth Bookmarking
If your statistics foundation is weak, you'll spend more time memorizing algorithms than understanding them.
Here are 12 resources that are genuinely worth your time.
1. Penn State STAT Online
Complete university-level statistics courses available for free.
2. Seeing Theory
Interactive visualizations that make probability and statistics much easier to grasp.
3. OpenIntro Statistics
A free, beginner-friendly statistics textbook with exercises.
4. Introduction to Statistical Learning (ISLR)
One of the best books for learning statistical learning with practical R and Python examples.
5. Khan Academy Statistics & Probability
Excellent if you're starting from scratch.
6. StatQuest
Probably the clearest explanations of statistics and machine learning on YouTube.
7. Stanford Online (Statistics Courses)
Free lectures covering probability and statistical thinking.
8. Harvard CS109 Data Science
Statistics taught through real-world data analysis.
9. ProbabilityCourse.com
One of the best free references for probability concepts.
10. Statistics by Jim
Straightforward articles explaining common statistical concepts.
11. NIST e-Handbook of Statistical Methods
A comprehensive reference used by engineers, researchers, and analysts.
12. Cross Validated (Stack Exchange)
When you're stuck on a statistics question, chances are it's already been answered here.
⭐️ Bookmark this.
@datascience_bds
