¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
Data Science & Machine Learning
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 For collaborations: @love_data
Mostrar más📈 Análisis del canal de Telegram Data Science & Machine Learning
El canal Data Science & Machine Learning (@datasciencefun) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 75 624 suscriptores, ocupando la posición 2 119 en la categoría Educación y el puesto 4 357 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 624 suscriptores.
Según los últimos datos del 10 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 922, y en las últimas 24 horas de 33, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.55%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.39% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 687 visualizaciones. En el primer día suele acumular 1 051 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
- Intereses temáticos: El contenido se centra en temas clave como learning, accuracy, distribution, panda, dataset.
📝 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
For collaborations: @love_data”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 11 junio, 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.
75 624
Suscriptores
+3324 horas
+2197 días
+92230 días
Archivo de publicaciones
𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 - 𝗚𝗲𝘁 𝗦𝗮𝗹𝗮𝗿𝘆 𝗣𝗮𝗰𝗸𝗮𝗴𝗲 𝗨𝗽𝘁𝗼 𝟰𝟭𝗟𝗣𝗔 😍
Upskill on the most in-demand skills in the market
Learn Coding & Get Placed In Top Tech Companies
𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:-
💼 Avg. Package: ₹7.2 LPA | Highest: ₹41 LPA
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰 👇:-
https://pdlink.in/42WOE5H
Hurry! Limited seats are available.🏃♂️
✅ Random Forest Basics🌲🤖
👉 Random Forest is one of the most popular and powerful Machine Learning algorithms.
It combines multiple Decision Trees to make better predictions.
🔹 1. What is Random Forest?
Random Forest = Collection of many Decision Trees
👉 Instead of relying on one tree, it takes predictions from many trees and gives the final result.
This improves:
✔ Accuracy
✔ Stability
✔ Performance
🔥 2. How Random Forest Works
Step-by-step:
1️⃣ Create multiple Decision Trees
2️⃣ Train each tree on random data samples
3️⃣ Each tree gives prediction
4️⃣ Final prediction = Majority vote (classification)
🔹 3. Example
👉 Predict if a customer will buy a product.
Tree 1 → Yes
Tree 2 → Yes
Tree 3 → No
✅ Final Prediction → Yes
🔹 4. Implementation (Python)
from sklearn.ensemble import RandomForestClassifier
# Sample data
X = [,,, ]
y = [1, 2, 3, 4, 0]
model = RandomForestClassifier()
model.fit(X, y)
print(model.predict([])[3])
💡 Level Up Your IT Career in 2026 – For FREE
Areas covered: #Python #AI #Cisco #PMP #Fortinet #AWS #Azure #Excel #CompTIA #ITIL #Cloud + more
🔗 Download each free resource here:
• Free Courses (Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS)
👉https://bit.ly/492lupg
• IT Certs E-book
👉https://bit.ly/4vXETS8
• IT Exams Skill Test
👉 https://bit.ly/4t1fhkB
• Free AI Materials & Support Tools
👉 https://bit.ly/4cWlwQL
• Free Cloud Study Guide
👉https://bit.ly/4cU6F9h
📲 Need exam help? Contact admin: wa.link/qse4fe
💬 Join our study group (free tips & support): https://chat.whatsapp.com/K3n7OYEXgT1CHGylN6fM5a
What type of problems can Decision Trees solve?
Which of the following is a disadvantage of Decision Trees?
Which library module is commonly used for Decision Trees in Python?
What is the starting node of a Decision Tree called?
What does a Decision Tree mainly use to make predictions?
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀😍
Kickstart Your Data Science Career In Top Tech Companies
💫Learn Tools, Skills & Mindset to Land your first Job
💫Join this free Masterclass for an expert-led session on Data Science
Eligibility :- Students ,Freshers & Working Professionals
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 :-
https://pdlink.in/42hIcpO
( Limited Slots ..Hurry Up )
🔥Date & Time :- 8th May 2026 , 7:00 PM
✅ Decision Trees Basics🌳🤖
👉 Decision Trees are one of the most intuitive ML algorithms — they work like a flowchart.
🔹 1. What is a Decision Tree?
A Decision Tree is a model that makes decisions by splitting data into branches.
👉 It asks questions like:
- Is age > 18?
- Is salary > 50k?
Based on answers → it predicts output.
🔥 2. Structure of a Decision Tree
🌳 Root Node → Starting point
🌿 Branches → Conditions (Yes/No)
🍃 Leaf Nodes → Final output
🔹 3. Example
👉 Predict if a person will buy a product:
Is Age > 30?
├── Yes → High Chance
└── No → Check Income
├── High → Medium Chance
└── Low → Low Chance
🔹 4. Types of Problems
✔ Classification (Yes/No)
✔ Regression (predict values)
🔹 5. Implementation (Python)
from sklearn.tree import DecisionTreeClassifier
# Sample data
X = [[25], [30], [45], [50]]
y = [0, 0, 1, 1]
model = DecisionTreeClassifier()
model.fit(X, y)
print(model.predict([[40]]))
🔹 6. Advantages ⭐
✔ Easy to understand
✔ No need for scaling
✔ Works with both numbers & categories
🔹 7. Disadvantages
❌ Can overfit (too complex tree)
❌ Sensitive to small data changes
🔹 8. Why Decision Trees are Important?
✔ Used in real-world ML systems
✔ Foundation for Random Forest & XGBoost
✔ Easy to explain to stakeholders
🎯 Today’s Goal
✔ Understand tree structure
✔ Learn splitting logic
✔ Implement basic model
💬 Tap ❤️ for more!
🚀 𝗭𝗲𝗿𝗼 𝗦𝗸𝗶𝗹𝗹𝘀 → 𝗢𝗻𝗹𝗶𝗻𝗲 𝗜𝗻𝗰𝗼𝗺𝗲 💸 (𝗔𝗜 𝗜𝘀 𝗗𝗼𝗶𝗻𝗴 𝗜𝘁 𝗔𝗹𝗹)
People are literally earning online by building apps… without coding
Now you can turn your ideas into websites & apps using AI in minutes 🔥
👉 No experience. No investment. Just execution.
✨ What you can do:
✔ Build apps & websites with AI 🤖
✔ Offer services & earn from clients 💰
✔ Start freelancing instantly
✔ Work from anywhere 🌍
🔥 Why this is blowing up:
• AI tools are replacing coding barriers
• Businesses are paying for fast solutions
• Huge demand + low competition (right now)
𝗦𝘁𝗮𝗿𝘁 𝗡𝗼𝘄👇:-
https://pdlink.in/4sRlP5d
💫 If you ignore this now, you’ll learn it later when it’s crowded
What is the range of output in Logistic Regression?
Logistic Regression is used for which type of problem?
💻 𝗙𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗲 𝗘𝗮𝗿𝗻𝗶𝗻𝗴 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 | 𝗕𝘂𝗶𝗹𝗱 𝗔𝗽𝗽𝘀 & 𝗘𝗮𝗿𝗻 𝗢𝗻𝗹𝗶𝗻𝗲
Imagine earning money by creating apps & websites using AI… without coding🔥
This platform lets you turn ideas into real apps in minutes 🤯
👉 Perfect for freelancers, beginners & side hustlers
🔥 Why you shouldn’t miss this:
* Zero investment to start
* High-demand skill (AI + freelancing)
* Unlimited earning potential
𝗦𝘁𝗮𝗿𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗵𝗲𝗿𝗲👇:-
https://pdlink.in/4e4ILub
💬 Your idea + AI = Your next income source 💸
✅ Logistic Regression Basics 🤖📊
👉 After predicting numbers (Linear Regression), now we predict categories.
🔹 1. What is Logistic Regression?
Logistic Regression is used for classification problems.
👉 Output is NOT a number — it’s a category.
Examples:
✔ Spam or Not Spam
✔ Pass or Fail
✔ Fraud or Not Fraud
🔥 2. How it Works
Instead of a straight line, it uses a Sigmoid Function:
\sigma(x) = 1 / (1 + e⁻)}
👉 Output is always between 0 and 1
👉 This is treated as probability
🔹 3. Decision Boundary
👉 If probability > 0.5 → Class 1
👉 If probability < 0.5 → Class 0
🔹 4. Example
👉 Predict if a student passes:
Study Hours Result
2 Fail
5 Pass
👉 Model learns boundary between pass/fail.
🔹 5. Implementation
from sklearn.linear_model import LogisticRegression
# Sample data
X = [[1], [2], [3], [4]]
y = [0, 0, 1, 1]
model = LogisticRegression()
model.fit(X, y)
print(model.predict([[3]]))
Which library is used for Linear Regression in Python?
