¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
Artificial Intelligence
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🔰 Machine Learning & Artificial Intelligence Free Resources 🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data
Mostrar más📈 Análisis del canal de Telegram Artificial Intelligence
El canal Artificial Intelligence (@machinelearning_deeplearning) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 53 094 suscriptores, ocupando la posición 3 252 en la categoría Educación y el puesto 7 063 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 53 094 suscriptores.
Según los últimos datos del 06 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 1 082, y en las últimas 24 horas de 17, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.70%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 027 visualizaciones. En el primer día suele acumular 0 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 11.
- Intereses temáticos: El contenido se centra en temas clave como learning, classification, layer, pattern, chatbot.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“🔰 Machine Learning & Artificial Intelligence Free Resources
🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 08 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.
53 094
Suscriptores
+1724 horas
+2037 días
+1 08230 días
Archivo de publicaciones
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Artificial Intelligence on WhatsApp 🚀
Top AI Channels on WhatsApp!
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2. OpenAI – Your gateway to cutting-edge artificial intelligence innovation. https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
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6. Prompt Engineering – Your secret weapon to get the best out of AI. https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
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8. AI Studio – Everything about AI & Tech https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
9. Google Gemini – Generate images & videos with AI. https://whatsapp.com/channel/0029Vb5Q4ly3mFY3Jz7qIu3i/103
10. Data Science & Machine Learning – Your fuel for insights, predictions, and smarter decisions. https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
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Statistics Roadmap for Data Science!
Phase 1: Fundamentals of Statistics
1️⃣ Basic Concepts
-Introduction to Statistics
-Types of Data
-Descriptive Statistics
2️⃣ Probability
-Basic Probability
-Conditional Probability
-Probability Distributions
Phase 2: Intermediate Statistics
3️⃣ Inferential Statistics
-Sampling and Sampling Distributions
-Hypothesis Testing
-Confidence Intervals
4️⃣ Regression Analysis
-Linear Regression
-Diagnostics and Validation
Phase 3: Advanced Topics
5️⃣ Advanced Probability and Statistics
-Advanced Probability Distributions
-Bayesian Statistics
6️⃣ Multivariate Statistics
-Principal Component Analysis (PCA)
-Clustering
Phase 4: Statistical Learning and Machine Learning
7️⃣ Statistical Learning
-Introduction to Statistical Learning
-Supervised Learning
-Unsupervised Learning
Phase 5: Practical Application
8️⃣ Tools and Software
-Statistical Software (R, Python)
-Data Visualization (Matplotlib, Seaborn, ggplot2)
9️⃣ Projects and Case Studies
-Capstone Project
-Case Studies
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𝟯 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱!😍
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SQL Basics for Data Analysts
SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.
1️⃣ Understanding Databases & Tables
Databases store structured data in tables.
Tables contain rows (records) and columns (fields).
Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).
2️⃣ Basic SQL Commands
Let's start with some fundamental queries:
🔹 SELECT – Retrieve Data
SELECT * FROM employees; -- Fetch all columns from 'employees' table SELECT name, salary FROM employees; -- Fetch specific columns SELECT * FROM employees WHERE department = 'Sales'; -- Filter by department SELECT * FROM employees WHERE salary > 50000; -- Filter by salary SELECT * FROM employees ORDER BY salary DESC; -- Sort by salary (highest first) SELECT name, hire_date FROM employees ORDER BY hire_date ASC; -- Sort by hire date (oldest first) SELECT * FROM employees LIMIT 5; -- Fetch only 5 rows SELECT * FROM employees WHERE department = 'HR' LIMIT 10; -- Fetch first 10 HR employees SELECT DISTINCT department FROM employees; -- Show unique departments 53 098
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53 098
SQL Basics for Data Analysts
SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.
1️⃣ Understanding Databases & Tables
Databases store structured data in tables.
Tables contain rows (records) and columns (fields).
Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).
2️⃣ Basic SQL Commands
Let's start with some fundamental queries:
🔹 SELECT – Retrieve Data
SELECT * FROM employees; -- Fetch all columns from 'employees' table SELECT name, salary FROM employees; -- Fetch specific columns SELECT * FROM employees WHERE department = 'Sales'; -- Filter by department SELECT * FROM employees WHERE salary > 50000; -- Filter by salary SELECT * FROM employees ORDER BY salary DESC; -- Sort by salary (highest first) SELECT name, hire_date FROM employees ORDER BY hire_date ASC; -- Sort by hire date (oldest first) SELECT * FROM employees LIMIT 5; -- Fetch only 5 rows SELECT * FROM employees WHERE department = 'HR' LIMIT 10; -- Fetch first 10 HR employees SELECT DISTINCT department FROM employees; -- Show unique departments 53 098
𝗪𝗶𝗽𝗿𝗼’𝘀 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿: 𝗬𝗼𝘂𝗿 𝗙𝗮𝘀𝘁-𝗧𝗿𝗮𝗰𝗸 𝘁𝗼 𝗮 𝗗𝗮𝘁𝗮 𝗖𝗮𝗿𝗲𝗲𝗿!😍
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✅ Data Science Roadmap for Beginners in 2025 🚀📊
1️⃣ Grasp the Role of a Data Scientist
🔍 Collect, clean, analyze data, build models, and communicate insights to drive decisions.
2️⃣ Master Python Basics
🐍 Learn:
– Variables, loops, functions
– Libraries: pandas, numpy, matplotlib
💡 Python is the most popular language in data science.
3️⃣ Learn SQL for Data Extraction
🧩 Focus on:
– SELECT, WHERE, JOIN, GROUP BY
– Practice on platforms like LeetCode or HackerRank.
4️⃣ Understand Statistics & Math
📊 Key topics:
– Descriptive statistics (mean, median, mode)
– Probability basics
– Hypothesis testing
💡 These are essential for building reliable models.
5️⃣ Explore Machine Learning Fundamentals
🤖 Start with:
– Supervised vs unsupervised learning
– Algorithms: Linear regression, decision trees
– Model evaluation metrics
6️⃣ Get Comfortable with Data Visualization
📈 Use tools like:
– Tableau or Power BI
– matplotlib and seaborn in Python
💡 Visuals help tell compelling data stories.
7️⃣ Work on Real-World Projects
🔍 Use datasets from Kaggle or UCI Machine Learning Repository
– Practice cleaning, analyzing, and modeling data.
8️⃣ Build Your Portfolio
💻 Showcase projects on GitHub or personal website
📌 Include code, visuals, and clear explanations.
9️⃣ Develop Soft Skills
🗣️ Focus on:
– Explaining technical concepts simply
– Problem-solving mindset
– Collaboration and communication
🔟 Earn Certifications to Boost Credibility
🎓 Consider:
– IBM Data Science Professional Certificate
– Google Data Analytics Certificate
– Coursera’s Machine Learning by Andrew Ng
🎯 Start applying for internships and junior roles
Positions like:
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– Junior Data Scientist
– Data Analyst
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10 Machine Learning Concepts You Must Know
✅ Supervised vs Unsupervised Learning – Understand the foundation of ML tasks
✅ Bias-Variance Tradeoff – Balance underfitting and overfitting
✅ Feature Engineering – The secret sauce to boost model performance
✅ Train-Test Split & Cross-Validation – Evaluate models the right way
✅ Confusion Matrix – Measure model accuracy, precision, recall, and F1
✅ Gradient Descent – The algorithm behind learning in most models
✅ Regularization (L1/L2) – Prevent overfitting by penalizing complexity
✅ Decision Trees & Random Forests – Interpretable and powerful models
✅ Support Vector Machines – Great for classification with clear boundaries
✅ Neural Networks – The foundation of deep learning
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𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁’𝘀 𝗙𝗥𝗘𝗘 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 – 𝗟𝗲𝗮𝗿𝗻 𝗛𝗼𝘄 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗪𝗼𝗿𝗸𝘀😍
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Python Interview Questions – Part 1
1. What is Python?
Python is a high-level, interpreted programming language known for its readability and wide range of libraries.
2. Is Python statically typed or dynamically typed?
Dynamically typed. You don't need to declare data types explicitly.
3. What is the difference between a list and a tuple?
List is mutable, can be modified.
Tuple is immutable, cannot be changed after creation.
4. What is indentation in Python?
Indentation is used to define blocks of code. Python strictly relies on indentation instead of brackets {}.
5. What is the output of this code?
x = [1, 2, 3]
print(x * 2)
Answer: [1, 2, 3, 1, 2, 3]
6. Write a Python program to check if a number is even or odd.
num = int(input("Enter number: "))
if num % 2 == 0:
print("Even")
else:
print("Odd")
7. What is a Python dictionary?
A collection of key-value pairs. Example:
person = {"name": "Alice", "age": 25}
8. Write a function to return the square of a number.
def square(n):
return n * n
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𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗙𝗮𝘀𝘁: 𝗟𝗲𝗮𝗿𝗻 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝘁𝗵 𝗣𝗿𝗼𝗷𝗲𝗰𝘁-𝗕𝗮𝘀𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟯𝟬 𝗗𝗮𝘆𝘀!😍
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🔍 Machine Learning Cheat Sheet 🔍
1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.
2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)
3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.
4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.
5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.
6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.
7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.
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𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗝𝘂𝘀𝘁 𝗥𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝟱 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻’𝘁 𝗠𝗶𝘀𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱!😍
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You won’t become an AI Engineer in a month.
You won’t suddenly build world-class systems after a bootcamp.
You won’t unlock next-level skills just by binge-watching tutorials for 30 days.
Because in a month, you’ll realize:
— Most of your blockers aren’t about “AI”, they’re about solid engineering: writing clean code, debugging, and shipping reliable software.
— Learning a new tool is easy; building things that don’t break under pressure is where people struggle.
— Progress comes from showing up every day, not burning out in a week.
So what should you actually do?
Here’s what works:
→ Spend 30 minutes daily on a core software skill.
One day, refactor old code for readability. Next, write unit tests. After that, dive into error handling or learn how to set up a new deployment pipeline.
→ Block out 3–4 hours every weekend to build something real.
Create a simple REST API. Automate a repetitive task. Try deploying a toy app to the cloud.
Don’t worry about perfection. Focus on finishing.
→ Each week, pick one engineering topic to dig into.
Maybe it’s version control, maybe it’s CI/CD, maybe it’s understanding how authentication actually works.
The goal: get comfortable with the “plumbing” that real software runs on.
You don’t need to cram.
You need to compound.
A little progress, done daily
That’s how you build confidence.
That’s how you get job-ready.
Small efforts. Done consistently.
That’s the unfair advantage you’re waiting to find, always has been.
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𝗪𝗮𝗻𝘁 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 — 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲?😍
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