¡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 75 764 suscriptores, ocupando la posición 2 114 en la categoría Educación y el puesto 4 334 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 764 suscriptores.
Según los últimos datos del 15 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 936, 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 3.44%. 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 2 606 visualizaciones. En el primer día suele acumular 1 052 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 16 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.
75 764
Suscriptores
+624 horas
+2237 días
+93630 días
Archivo de publicaciones
Cool! Let’s jump into K-Nearest Neighbors (KNN) — the friendly, simple, but surprisingly smart algorithm.
Let's say, You move into a new neighborhood and you want to figure out what kind of food the locals like.
So, you knock on the doors of your nearest 5 neighbors and ask them.
If 3 say “we love pizza” and 2 say “we love sushi,” you assume — “Alright, this area probably loves pizza.”
That’s how KNN works.
How It Works:
Let’s say you have a bunch of data points (people, items, whatever) and each one is labeled — like:
This customer bought the product.
This one didn’t.
Now you get a new customer and want to predict if they’ll buy.
KNN looks at the K closest points (neighbors) in the data — maybe 3, 5, or 7 — and checks:
What decision did those neighbors make?
Whichever label is in the majority becomes the prediction for the new one.
Simple voting system — based on closeness.
But Wait, What’s “Nearest”?
It means:
Whose values (like age, income, etc.) are most similar?
“Closeness” is measured using math — like distance in space.
So, it’s not literal neighbors — it’s more like “closest match” in the data.”
Where It Works Well:
Classifying handwritten digits (0–9)
Recommendation systems
Face recognition
When you need something simple but effective
The beauty? No training phase! It just stores the data and looks around at prediction time.
React with ♥️ if you're ready for the next algorithm, Support Vector Machines (SVM). It’s like drawing the cleanest line possible between two groups.
𝟰 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗙𝗿𝗲𝗲 𝗦𝗤𝗟 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀😍
Struggling to learn SQL as a beginner data analyst? You’re not alone — and you don’t have to stay stuck👋
Here are 4 top-notch, beginner-friendly SQL courses that are 100% free🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44lQvmw
Enroll For FREE & Get Certified 🎓️
Let’s go — time for Random Forest, one of the most powerful and popular algorithms out there!
Let's say, You want to make an important decision — so instead of asking just one person, you ask 100 people and go with the majority opinion.
That’s Random Forest in a nutshell.
It builds many decision trees, lets them all vote, and then takes the most popular answer.
Why?
Because relying on just one decision tree can be risky — it might overfit (aka learn too much from the training data and mess up on new data).
But if you build many trees on slightly different pieces of data, each one learns something different. When you bring all their results together, the final answer is way more accurate and balanced.
It’s like:
One tree might make a mistake.
But a forest of trees? Much smarter together.
Real-Life Analogy:
Let’s say you’re trying to decide which laptop to buy.
You ask one friend (that’s like a decision tree).
Or you ask 10 friends, each with different experiences, and you go with what most of them say (that’s a random forest).
You’ll feel a lot more confident in your decision, right?
That’s exactly what this algorithm does.
Where to use it:
- Predicting whether someone will default on a loan
- Detecting fraud
- Recommending products
Any place where accuracy really matters
It’s a bit heavier computationally, but the trade-off is often worth it.
Ready with ♥️ if you're want me to cover all ML Algorithms
Up next: K-Nearest Neighbors (KNN) — the friendly neighbor algorithm!
𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐏𝐫𝐨𝐠𝐫𝐚𝐦😍
Learn Full Stack Development from IIT Alumni & Top Tech Experts.
𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:-
60+ Hiring Drives Every Month
🌟 Trusted by 7500+ Students
🤝 500+ Hiring Partners
💼 Avg. Package: ₹7.2 LPA | Highest: ₹41 LPA
Eligibility: BTech / BCA / BSc / MCA / MSc
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰 👇:-
https://pdlink.in/4hO7rWY
Hurry! Limited seats available. 🏃♀️
Alright, let’s get into Decision Trees — one of the easiest and most intuitive ML algorithms out there.
Think of it like this:
You're playing 20 Questions — where each question helps you narrow down the possibilities. Decision Trees work just like that.
It’s like teaching a computer how to ask smart questions to reach an answer.
Real-Life Example:
Say you’re trying to decide whether to go for a walk.
Your brain might go:
Is it raining?
→ Yes → Stay home.
→ No → Next question.
Is it too hot?
→ Yes → Stay home.
→ No → Go for a walk.
This “question-answer” logic is exactly how a Decision Tree works.
It keeps splitting the data based on the most useful questions — until it reaches a decision.
In ML Terms (Still super simple):
Let’s say you’re building a model to predict if someone will buy a product online.
The decision tree might ask:
Is their age above 30?
Did they visit the website more than 3 times this week?
Do they have items in their cart?
Depending on the answers (yes/no), the tree branches out until it reaches a final decision: Buy or Not Buy.
Why It’s Cool:
Easy to understand and explain (no complex math).
Works for both classification (yes/no) and regression (predicting numbers).
Looks just like a flowchart — very visual.
But there’s a twist: one tree is cool, but a bunch of trees is even better.
Shall we talk about that next? It’s called Random Forest — and it’s like a team of decision trees working together.
React with ❤️ if you want me to explain Random Forest
Alright, let’s get into Decision Trees — one of the easiest and most intuitive ML algorithms out there.
Think of it like this:
You're playing 20 Questions — where each question helps you narrow down the possibilities. Decision Trees work just like that.
It’s like teaching a computer how to ask smart questions to reach an answer.
Real-Life Example:
Say you’re trying to decide whether to go for a walk.
Your brain might go:
Is it raining?
→ Yes → Stay home.
→ No → Next question.
Is it too hot?
→ Yes → Stay home.
→ No → Go for a walk.
This “question-answer” logic is exactly how a Decision Tree works.
It keeps splitting the data based on the most useful questions — until it reaches a decision.
In ML Terms (Still super simple):
Let’s say you’re building a model to predict if someone will buy a product online.
The decision tree might ask:
Is their age above 30?
Did they visit the website more than 3 times this week?
Do they have items in their cart?
Depending on the answers (yes/no), the tree branches out until it reaches a final decision: Buy or Not Buy.
Why It’s Cool:
Easy to understand and explain (no complex math).
Works for both classification (yes/no) and regression (predicting numbers).
Looks just like a flowchart — very visual.
But there’s a twist: one tree is cool, but a bunch of trees is even better.
Shall we talk about that next? It’s called Random Forest — and it’s like a team of decision trees working together.
React with ❤️ if you want me to explain Random Forest
𝗧𝗼𝗽 𝟯 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝘄𝗶𝘁𝗵 𝗘𝘅𝗰𝗲𝗹, 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 & 𝗧𝗮𝗯𝗹𝗲𝗮𝘂)😍
Breaking into data analytics can feel overwhelming—especially when you’re unsure where to begin or what tools to learn. But what if we told you that you can master the top tools Excel, Power BI, and Tableau completely for FREE?🎯
Let’s break down each course 👇
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4loaBTc
🎯 Final Thoughts: Learn Smart. Get Certified. Land the Job.
Let’s move on to the next one: Logistic Regression.
And don’t worry — even though it sounds like “linear regression,” this one’s all about yes or no answers.
What is Logistic Regression?
Let’s say you want to predict if someone will get approved for a loan or not.
You’ve got details like:
Their income
Credit score
Employment status
But the final output is binary — either “Yes” (approved) or “No” (not approved).
That’s where Logistic Regression comes in. It’s used when the outcome is yes/no, true/false, 0/1 — anything with just two categories.
Real-Life Vibe:
Imagine you’re trying to figure out if a student will pass or fail an exam based on the number of hours they study.
Now instead of drawing a straight line (like in linear regression), logistic regression draws an S-shaped curve.
Why?
Because we want to squeeze all predictions into a range between 0 and 1 — where:
Closer to 1 = high chance of “Yes”
Closer to 0 = high chance of “No”
For example:
If the model says 0.95 → Very likely to pass
If it says 0.20 → Not likely to pass
You can set a cut-off point, say 0.5 — anything above that is considered “Yes,” and below it is “No.”
It’s the go-to model for problems like:
Will the customer churn?
Is this email spam?
Will the patient have a disease?
Simple, fast, and surprisingly powerful.
React with ♥️ if you want me to cover the next one — Decision Trees!
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
Learn from top faculty & experts - Become a skilled professional
- Learn from the best
- Learn by doing
- Learn with AI
Get FREE Course Review & Start Learning
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/41VIuSA
Enroll Now & Get a Course Completion Certification🎓
Now let's understand Linear Regression in detail.
Linear Regression is all about predicting a continuous value (like salary, price, temperature) based on another variable (like years of experience, number of products sold, etc.).
Let's say, You’re trying to predict someone's salary based on their years of experience. As experience increases, you generally expect the salary to increase too. What linear regression does is find the best line that fits this trend.
The line is represented by this simple equation:
Salary = m * Years of Experience + b
Here:
m is the slope of the line (it tells you how much salary increases with each additional year of experience).
b is the y-intercept (the starting point, or the salary when there's no experience).
The Process:
Training the model: The algorithm looks at all your data and tries to draw the straightest line possible that fits the pattern between experience and salary. It does this by adjusting the m (slope) and b (intercept) to minimize the difference between predicted and actual salaries.
Making predictions: Once the model has learned the best line, it can predict salaries for new people based on their years of experience. For example, if you tell it someone has 5 years of experience, it will give you the predicted salary.
Linear regression is great when there's a straight-line relationship between variables. It helps you make predictions, and because it’s simple, it’s often used as a starting point for many problems.
React with ♥️ if you need similar explanation for the rest of the algorithms
𝗔𝗰𝗰𝗲𝗻𝘁𝘂𝗿𝗲 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 😍
- Data Analytics and Visualization
- Coding: Development
- Project Management
- Software Engineering
These are perfect for students, freshers, or job seekers looking to stand out in a competitive job market.
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/3EmuAkw
Enroll For FREE & Get Certified🎓
So now that you know what machine learning is (teaching computers to learn from data), the next thing is.
How do they learn?
That’s where algorithms come in.
Think of algorithms as different learning styles.
Just like people — some learn best by watching videos, others by solving problems — computers have different ways to learn too. These different ways are what we call machine learning algorithms.
Let’s start with the most common and simple ones.
I’ll explain them one by one in a way that makes sense.
Here’s a quick list of popular ML algorithms:
Linear Regression – predicts numbers (like house prices).
Logistic Regression – predicts categories (yes/no, spam/not spam).
Decision Trees – makes decisions by asking questions.
Random Forest – a group of decision trees working together.
K-Nearest Neighbors (KNN) – looks at neighbors to decide.
Support Vector Machine (SVM) – draws lines to separate data.
Naive Bayes – based on probability, good for text (like spam filters).
K-Means Clustering – groups similar things together.
Principal Component Analysis (PCA) – reduces complexity of data.
Neural Networks – the backbone of deep learning (used in face recognition, voice assistants, etc.).
Wanna need a detailed explanation on each algorithm?
React with ♥️ and let me know in the comments if you really want to learn more about the algorithms.
Today, lets understand Machine Learning in simplest way possible
What is Machine Learning?
Think of it like this:
Machine Learning is when you teach a computer to learn from data, so it can make decisions or predictions without being told exactly what to do step-by-step.
Real-Life Example:
Let’s say you want to teach a kid how to recognize a dog.
You show the kid a bunch of pictures of dogs.
The kid starts noticing patterns — “Oh, they have four legs, fur, floppy ears...”
Next time the kid sees a new picture, they might say, “That’s a dog!” — even if they’ve never seen that exact dog before.
That’s what machine learning does — but instead of a kid, it's a computer.
In Tech Terms (Still Simple):
You give the computer data (like pictures, numbers, or text).
You give it examples of the right answers (like “this is a dog”, “this is not a dog”).
It learns the patterns.
Later, when you give it new data, it makes a smart guess.
Few Common Uses of ML You See Every Day:
Netflix: Suggesting shows you might like.
Google Maps: Predicting traffic.
Amazon: Recommending products.
Banks: Detecting fraud in transactions.
Should we start covering all data Science and machine learning concepts like this?
𝗜𝗻𝗱𝗶𝗮'𝘀 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗗𝗿𝗶𝘃𝗲 𝗙𝗼𝗿 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗦𝘁𝘂𝗱𝗲𝗻𝘁𝘀 😍
Get Recognition from Top Companies like Emerson, Flex, TVS, Cargill & Many More
✅ Win Prizes Worth Rs 20 Lacs
✅ Paid Internship with Top MNCs
✅ Recognition from Top Companies
✅ Make Your CV Stand Out!
Eligibility: - Students Currently Pursuing UG/PG Courses
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
https://pdlink.in/3RHDZ9r
Golden Opportunity for All College Students 💫
🌮 Data Analyst Vs Data Engineer Vs Data Scientist 🌮
Skills required to become data analyst
👉 Advanced Excel, Oracle/SQL
👉 Python/R
Skills required to become data engineer
👉 Python/ Java.
👉 SQL, NoSQL technologies like Cassandra or MongoDB
👉 Big data technologies like Hadoop, Hive/ Pig/ Spark
Skills required to become data Scientist
👉 In-depth knowledge of tools like R/ Python/ SAS.
👉 Well versed in various machine learning algorithms like scikit-learn, karas and tensorflow
👉 SQL and NoSQL
Bonus skill required: Data Visualization (PowerBI/ Tableau) & Statistics
