Data Science & Machine Learning Resources
Ir al canal en Telegram
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free Admin: @love_data Buy ads: https://telega.io/c/datalemur
Mostrar más
Red:📈 Análisis del canal de Telegram Data Science & Machine Learning Resources
El canal Data Science & Machine Learning Resources (@datalemur) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 20 597 suscriptores, ocupando la posición 9 687 en la categoría Educación y el puesto 20 482 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 20 597 suscriptores.
Según los últimos datos del 30 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 127, y en las últimas 24 horas de 6, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.66%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.68% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 547 visualizaciones. En el primer día suele acumular 140 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
- Intereses temáticos: El contenido se centra en temas clave como |--, learning, insidead, database, sql.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
Admin: @love_data
Buy ads: https://telega.io/c/datalemur”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 31 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.
20 597
Suscriptores
+624 horas
+447 días
+12730 días
Carga de datos en curso...
Canales Similares
Nube de Etiquetas
Menciones Entrantes y Salientes
---
---
---
---
---
---
Atraer Suscriptores
julio '26
julio '26
+239
en 3 canales
junio '26
+150
en 0 canales
Get PRO
mayo '26
+344
en 3 canales
Get PRO
abril '26
+188
en 0 canales
Get PRO
marzo '26
+262
en 1 canales
Get PRO
febrero '26
+433
en 1 canales
Get PRO
enero '26
+637
en 0 canales
Get PRO
diciembre '25
+494
en 0 canales
Get PRO
noviembre '25
+630
en 3 canales
Get PRO
octubre '25
+650
en 1 canales
Get PRO
septiembre '25
+656
en 1 canales
Get PRO
agosto '25
+839
en 4 canales
Get PRO
julio '25
+1 110
en 6 canales
Get PRO
junio '25
+1 989
en 9 canales
Get PRO
mayo '25
+2 717
en 7 canales
Get PRO
abril '25
+3 878
en 5 canales
Get PRO
marzo '25
+1 119
en 3 canales
Get PRO
febrero '25
+1 084
en 7 canales
Get PRO
enero '25
+1 436
en 5 canales
Get PRO
diciembre '24
+132
en 0 canales
Get PRO
noviembre '24
+551
en 0 canales
Get PRO
octubre '24
+648
en 0 canales
Get PRO
septiembre '24
+1 255
en 0 canales
Get PRO
agosto '24
+162
en 1 canales
| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 31 julio | +8 | |||
| 30 julio | +6 | |||
| 29 julio | +14 | |||
| 28 julio | +9 | |||
| 27 julio | +10 | |||
| 26 julio | +1 | |||
| 25 julio | +12 | |||
| 24 julio | +10 | |||
| 23 julio | +8 | |||
| 22 julio | +12 | |||
| 21 julio | +14 | |||
| 20 julio | +16 | |||
| 19 julio | +12 | |||
| 18 julio | +7 | |||
| 17 julio | +8 | |||
| 16 julio | +5 | |||
| 15 julio | +11 | |||
| 14 julio | +2 | |||
| 13 julio | +8 | |||
| 12 julio | +2 | |||
| 11 julio | +5 | |||
| 10 julio | +1 | |||
| 09 julio | +5 | |||
| 08 julio | +9 | |||
| 07 julio | +4 | |||
| 06 julio | +12 | |||
| 05 julio | +7 | |||
| 04 julio | +18 | |||
| 03 julio | 0 | |||
| 02 julio | +3 | |||
| 01 julio | 0 |
Publicaciones del Canal
Data Science Roadmap: 🗺
📂 Math & Stats
∟📂 Python/R
∟📂 Data Wrangling
∟📂 Visualization
∟📂 ML
∟📂 DL & NLP
∟📂 Projects
∟ ✅ Apply For Job
Like if you need detailed explanation step-by-step ❤️
| 2 |  If you’re a student, graduate, or someone looking for a career switch, read this.
Most people spend months watching random YouTube videos and still don’t become job-ready.
Instead, learn in a structured offline classroom.
📌 Data Analytics with GenAI
📌 Python + SQL + Power BI
📌 6-Month Program
📌 1:1 Mentorship
📌 Job Assistance
📍Now available in your city.
Seats are limited.
👉 Register Here: https://lp.pwskills.com/data-analytics-course-offline-batch0?utm_source=telegram&utm_medium=influencer&utm_campaign=daoffline | 382 |
| 3 | Top 10 machine Learning algorithms 👇👇
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
Credits: https://t.me/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊 | 576 |
| 4 |  Aaj hi ek certified Hackar bano!💻
Shuru se saari cheeze seekho bilkul basic se!!
PW skills leke aaya h certified Ethical Hacking ka course!!
Isme milega :
✅ Hands on Practice
✅ LIVE Hacking Labs
✅ Certificate after Completion
Sirf Rs 4999 mai
Abhi enroll karo HACK30 Coupon code use karke 30% OFF milega!
Enroll NOW : https://pwskills.com/web-development/certified-ethical-hacking-course-035473/?source=pwskills.com&position=course_dropdown&from=home_page&utm_source=pwskills&utm_medium=telegram&utm_campaign=ethical_hacking | 544 |
| 5 | 🎯Free AI/ML learning resources 👇
1/ Google Machine Learning Crash Course:
https://developers.google.com/machine-learning/crash-course
2/ Kaggle Learn:
https://www.kaggle.com/learn
3/ Harvard CS50 AI with Python:
https://cs50.harvard.edu/ai/
4/ DeepLearning.AI Short Courses:
https://www.deeplearning.ai/courses/
5/ TensorFlow Official Tutorials:
https://www.tensorflow.org/tutorials | 893 |
| 6 | Data Science Interview Questions.pdf | 951 |
| 7 | SQL vs Python Programming: Quick Comparison ✍
📌 SQL Programming
• Query data from databases
• Filter, join, aggregate rows
Best fields
• Data Analytics
• Business Intelligence
• Reporting and MIS
• Entry-level Data Engineering
Job titles
• Data Analyst
• Business Analyst
• BI Analyst
• SQL Developer
Hiring reality
• Asked in most analyst interviews
• Used daily in analyst roles
India salary range
• Fresher: 4–8 LPA
• Mid-level: 8–15 LPA
Real tasks
• Monthly sales report
• Top customers by revenue
• Duplicate removal
📌 Python Programming
• Clean and analyze data
• Automate workflows
• Build models
Where you work
• Notebooks
• Scripts
• ML pipelines
Best fields
• Data Science
• Machine Learning
• Automation
• Advanced Analytics
Job titles
• Data Scientist
• ML Engineer
• Analytics Engineer
• Python Developer
Hiring reality
• Common in mid to senior roles
• Strong demand in AI teams
India salary range
• Fresher: 6–10 LPA
• Mid-level: 12–25 LPA
Real tasks
• Churn prediction
• Report automation
• File handling CSV, Excel, JSON
⚔️ Quick comparison
• Data source
SQL stays inside databases
Python pulls data from anywhere
• Speed
SQL runs fast on large tables
Python slows with raw big data
• Learning
SQL is beginner-friendly
Python needs coding basics
🎯 Role-based choice
• Data Analyst
SQL required
Python adds value
• Data Scientist
Python required
SQL used to fetch data
• Business Analyst
SQL works for most roles
Python helps automate work
• Data Engineer
SQL for pipelines
Python for processing
✅ Best career move
• Learn SQL first for entry
• Add Python for growth
• Use both in real projects
Which one do you prefer?
SQL 👍
Python ❤️
Both 🙏
None 😮 | 833 |
| 8 |  🎰 Welcome Bonus 1200% — Maczo Crypto Casino
🎮 Crypto exchange · Sports · Live casino — all in one place
💳 USDT instant deposit & withdrawal
→ https://tglink.io/b1074395ffd098 | 217 |
