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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
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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 13 905 suscriptores, ocupando la posición 8 986 en la categoría Tecnologías y Aplicaciones y el puesto 29 300 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 13 905 suscriptores.
Según los últimos datos del 25 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 109, y en las últimas 24 horas de 1, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.77%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.06% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 080 visualizaciones. En el primer día suele acumular 287 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
- 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 26 agosto, 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.
13 905
Suscriptores
+124 horas
+17 días
+10930 días
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Publicaciones del Canal
📈 Mean vs Median
Suppose these are five salaries:
$35k, $38k, $42k, $44k, $2.5MMean (average): $531,800 Median (middle value): $42,000 The average suggests everyone is wealthy. The median tells a completely different story. 👉 Whenever your data contains extreme values (called outliers), the median often represents the data much better than the mean. That's why you'll often see median house prices and median income reported in the news.
| 2 |  SQL CHART | 396 |
| 3 |  📉 Why We Split Data
If you train and evaluate a model using the exact same dataset, you're only testing how well it remembers.
That's why datasets are usually split into:
👉 Training set → The model learns from this.
👉 Validation set → Used to tune model settings.
👉 Test set → Used only once at the end to measure real performance.
Think of it like studying for an exam.
Reading the textbook is training. Practice questions are validation. The final exam is the test set. | 496 |
| 4 |  🐼 Pandas: The Dangerous Difference Between loc and iloc
Both select data. That's why beginners mix them up.
The simplest way to remember is:
loc → labels
iloc → positions
df.loc[5]
means:
Give me the row whose label is 5.
On the other hand:
df.iloc[5]
means:
Give me the 6th row.
Those are not necessarily the same row. Especially after filtering.
If your DataFrame index looks like:
0
1
4
7
9
then:
df.iloc[2]
returns the row at position 2. That's index label 4.
This tiny distinction causes a surprising number of bugs. | 489 |
| 5 | 📊 10 Websites Every Data Scientist Should Bookmark
Whether you're learning data science or building production models, they'll save you a lot of time.
Google Dataset Search
Find millions of public datasets from universities, governments, and research organizations.
Our World in Data
High quality datasets with well-researched visualizations on health, climate, economics, energy, education, and more.
UCI Machine Learning Repository
One of the most widely used collections of datasets for machine learning practice and research.
Papers with Code
Research papers linked with official implementations, datasets, and benchmark leaderboards.
OpenML
A platform for sharing datasets, experiments, and reproducible machine learning workflows.
Data.gov
Over 300,000 public datasets published by the U.S. government.
Awesome Public Datasets
A massive GitHub repository of datasets organized by category.
Google Colab
Run Python notebooks in the cloud with free GPU access for many workloads.
Hugging Face Datasets
Thousands of ready-to-use datasets for NLP, computer vision, audio, and more.
Kaggle Datasets
Millions of datasets shared by the data science community.
⭐️ Save this post. You'll probably use these throughout your data science journey. | 573 |
| 6 |  12 AI Frameworks Every AI Engineer Should Know | 618 |
| 7 |  📘 R for Data Science
✍️ Authors: Garrett Grolemund, Hadley Wickham
🔗 Read Online
#Datascience #R
────────────────────
👉 @free_programming_books_bds 👈 | 616 |
| 8 | ✅ SQL Essentials for Data Science 🗄
👉 SQL remains an absolute must-have skill for anyone working in Data Science or Analytics.
Virtually every organization manages its core information inside databases, and SQL is the key to extracting, transforming, and analyzing that data.
🔹 1. What is SQL?
SQL = Structured Query Language
👉 Used to:
✔️ Query data
✔️ Filter records
✔️ Perform calculations
✔️ Uncover business insights
🔥 2. Popular Database Engines
✔️ PostgreSQL
✔️ MySQL
✔️ Snowflake
✔️ Google BigQuery
🔹 3. Basic SQL Query
✅ The SELECT Clause
Used to fetch records from a table.
SELECT * FROM customers;
👉 * retrieves every single column.
🔹 4. Fetch Specific Columns
SELECT full_name, total_spent FROM customers;
🔹 5. WHERE Clause
Used to apply filters to your data.
SELECT * FROM customers WHERE age >= 25;
🔹 6. ORDER BY
Sort your results.
SELECT * FROM customers ORDER BY total_spent DESC;
✔️ ASC → Ascending (Lowest to Highest)
✔️ DESC → Descending (Highest to Lowest)
🔹 7. Aggregate Functions
Used for summary statistics.
Function: COUNT()
Purpose: Counts the number of rows
Function: SUM()
Purpose: Adds values together
Function: AVG()
Purpose: Finds the mean value
Function: MAX()
Purpose: Finds the highest value
Function: MIN()
Purpose: Finds the lowest value
✅ Example
SELECT AVG(total_spent) FROM customers;
🔹 8. GROUP BY
Used to categorize data into buckets.
SELECT country, SUM(total_spent) FROM customers GROUP BY country;
🔹 9. Why SQL is Critical?
✔️ #1 requested technical skill in job descriptions
✔️ Used daily by analysts, data engineers, & data scientists
✔️ Scales seamlessly with massive enterprise datasets | 601 |
| 9 |  ❌ Cross Entropy Isn't Measuring Accuracy
Here's something that surprises a lot of people. These two predictions are both correct.
Prediction A
Cat: 51%
Dog: 49%
Prediction B
Cat: 99.9%
Dog: 0.1%
Accuracy treats them exactly the same. Cross Entropy doesn't. It rewards confidence only when the model is correct.
If the true class is "Cat":
Prediction A gets a relatively high loss. Prediction B gets a very small loss.
Now flip the prediction.
Cat: 0.1%
Dog: 99.9%
The loss explodes. That's because Cross Entropy isn't asking:
Did you get it right?
It's asking:
How confident were you in the correct answer?
That's why neural networks optimize Cross Entropy instead of accuracy.
Accuracy is too coarse to guide learning. | 608 |
| 10 |  ML Engineer vs AI Engineer | 754 |
| 11 | 📍If Your Model Suddenly Gets Worse, Check These First
Before retraining everything, inspect:
• Data drift
• Missing values
• Feature distribution changes
• New categories
• Pipeline failures
• Label quality
Production issues are often data problems, not algorithm problems. | 998 |
| 12 |  How Does Machine Learning Work? | 1 035 |
| 13 |  15 GitHub Repositories For Machine Learning Engineers | 1 073 |
| 14 | List of AI Project Ideas 👨🏻💻🤖 -
Beginner Projects
🔹 Sentiment Analyzer
🔹 Image Classifier
🔹 Spam Detection System
🔹 Face Detection
🔹 Chatbot (Rule-based)
🔹 Movie Recommendation System
🔹 Handwritten Digit Recognition
🔹 Speech-to-Text Converter
🔹 AI-Powered Calculator
🔹 AI Hangman Game
Intermediate Projects
🔸 AI Virtual Assistant
🔸 Fake News Detector
🔸 Music Genre Classification
🔸 AI Resume Screener
🔸 Style Transfer App
🔸 Real-Time Object Detection
🔸 Chatbot with Memory
🔸 Autocorrect Tool
🔸 Face Recognition Attendance System
🔸 AI Sudoku Solver
Advanced Projects
🔺 AI Stock Predictor
🔺 AI Writer (GPT-based)
🔺 AI-powered Resume Builder
🔺 Deepfake Generator
🔺 AI Lawyer Assistant
🔺 AI-Powered Medical Diagnosis
🔺 AI-based Game Bot
🔺 Custom Voice Cloning
🔺 Multi-modal AI App
🔺 AI Research Paper Summarizer
@datascience_bds | 1 052 |
| 15 | +1 We recently had a request from for Unsupervised Learning notes.
To make this resource even more valuable for everyone, we decided to bundle them together with our Supervised Learning notes as well!
Source: Princeton University Lecture Notes
@datascience_bds | 991 |
| 16 |  ETL Process For Data Analytics | 1 137 |
| 17 | ✅ The Most Underrated Habit in Data Science
👉 Keep a modeling journal.
After every experiment, write down:
• What changed
• Why you changed it
• The metric before
• The metric after
• What you learned
Six months later, this notebook becomes more valuable than your code. | 1 184 |
| 18 | The Little Book of Deep Learning.pdf | 1 247 |
| 19 |  Power BI vs Microsoft Fabric | 1 343 |
| 20 | 🚩7 Red Flags You Should Check in Every Dataset
Before EDA, look for these.
🔻Duplicate rows
🔻Missing values that aren't random
🔻Impossible numbers (negative ages, future dates)
🔻Columns with only one value
🔻Categories with inconsistent spelling
🔻Target leakage
🔻Suspiciously perfect distributions
Catching these early saves hours of debugging later. | 1 351 |
