¡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 53 161 suscriptores, ocupando la posición 3 256 en la categoría Educación y el puesto 7 041 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 53 161 suscriptores.
Según los últimos datos del 09 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 1 045, y en las últimas 24 horas de 38, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.69%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.68% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 022 visualizaciones. En el primer día suele acumular 892 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 9.
- 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 10 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.
53 161
Suscriptores
+3824 horas
+1977 días
+1 04530 días
Archivo de publicaciones
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8 FREE AI Courses by Google 🎓🚀 Learn, Grow, and Succeed
1. Introduction to Generative AI
→ An introductory course to explain what generative AI is.
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2. Image Generation
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4. Large Language Models
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6. Attention Mechanism
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8. Image recognition
→ Learn how to create an AI that understands images.
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All the best 👍👍
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𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗧𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍
1️⃣ BCG Data Science & Analytics
2️⃣ TATA Data Visualization Internship
3️⃣ Accenture Data Analytics
4️⃣ PwC Power BI Internship
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6️⃣ Quantium Data Analytics
𝐋𝐢𝐧𝐤 👇:-
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Enroll For FREE & Get Certified 🎓
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𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
- SQL
- Blockchain
- HTML & CSS
- Excel, and
- Generative AI
These free full courses will take you from beginner to expert!
𝐋𝐢𝐧𝐤 👇:-
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53 173
Underrated Telegram Channel for Data Analysts 👇👇
https://t.me/sqlspecialist
Here, you will get free tutorials to learn SQL, Python, Power BI, Excel and many more
Hope you guys will like it 😄
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To automate your daily tasks using ChatGPT, you can follow these steps:
1. Identify Repetitive Tasks: Make a list of tasks that you perform regularly and that can potentially be automated.
2. Create ChatGPT Scripts: Use ChatGPT to create scripts or workflows for automating these tasks. You can use the API to interact with ChatGPT programmatically.
3. Integrate with Other Tools: Integrate ChatGPT with other tools and services that you use to streamline your workflow. For example, you can connect ChatGPT with task management tools, calendar apps, or communication platforms.
4. Set up Triggers: Set up triggers that will initiate the automated tasks based on certain conditions or events. This could be a specific time of day, a keyword in a message, or any other criteria you define.
5. Test and Iterate: Test your automated workflows to ensure they work as expected. Make adjustments as needed to improve efficiency and accuracy.
6. Monitor Performance: Keep an eye on how well your automated tasks are performing and make adjustments as necessary to optimize their efficiency.
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𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗙𝗥𝗘𝗘 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗩𝗶𝗱𝗲𝗼𝘀!😍
Want to become a Data Analytics pro?🔥
These tutorials simplify complex topics into easy-to-follow lessons✨️
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No more excuses—just pure learning!✅️
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+3
🔗 Master 8 Essential Machine Learning Algorithms
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It's crucial to dive deeper into their real-world applications and understand how AI is shaping the future.That's where "The Most Effective Guide to Master AI" comes in! This comprehensive guide covers everything you need to know: - Real-world AI applications - Computer Vision - Generative Models - Essential AI tools
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𝗠𝗮𝘀𝘁𝗲𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 – 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲!😍
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🚀 Here’s your step-by-step guide! From simple coding to hands-on projects and expert topics.
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Essential Data Analysis Techniques Every Analyst Should Know
1. Descriptive Statistics: Understanding measures of central tendency (mean, median, mode) and measures of spread (variance, standard deviation) to summarize data.
2. Data Cleaning: Techniques to handle missing values, outliers, and inconsistencies in data, ensuring that the data is accurate and reliable for analysis.
3. Exploratory Data Analysis (EDA): Using visualization tools like histograms, scatter plots, and box plots to uncover patterns, trends, and relationships in the data.
4. Hypothesis Testing: The process of making inferences about a population based on sample data, including understanding p-values, confidence intervals, and statistical significance.
5. Correlation and Regression Analysis: Techniques to measure the strength of relationships between variables and predict future outcomes based on existing data.
6. Time Series Analysis: Analyzing data collected over time to identify trends, seasonality, and cyclical patterns for forecasting purposes.
7. Clustering: Grouping similar data points together based on characteristics, useful in customer segmentation and market analysis.
8. Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) to reduce the number of variables in a dataset while preserving as much information as possible.
9. ANOVA (Analysis of Variance): A statistical method used to compare the means of three or more samples, determining if at least one mean is different.
10. Machine Learning Integration: Applying machine learning algorithms to enhance data analysis, enabling predictions, and automation of tasks.
Like this post if you need more 👍❤️
Hope it helps :)
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𝗙𝗥𝗘𝗘 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
1)Business Analysis – Foundation
2)Business Analysis Fundamentals
3)The Essentials of Business & Risk Analysis
4)Master Microsoft Power BI
𝗟𝗶𝗻𝗸 👇:-
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"I am an AI Tools & ChatGPT Expert, and my salary package is 42 LPA."
Sounds familiar? If you’ve been on YouTube recently, I’m sure you’ve seen this ad at least 100 times. Now, I have just one simple question – Can someone please tell me which companies are hiring for this role and paying 42 LPA? Because I’m also considering a career switch! 😂
See guys, learning how to use a few AI tools won't magically get you a 42 LPA job. Selling courses isn’t wrong, but selling them by giving false hopes is. Just because someone tells you that learning how to use a few AI tools will instantly land you a high-paying job doesn’t make it true.
So, a humble request – don’t fall for these unrealistic promises. Invest in courses only to upskill yourself, not with the expectation of overnight success.
If anyone actually finds this 42 LPA AI Tools & ChatGPT Expert job, please let me know. I’ll also update my resume! 🤣
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Master AI in 2025 – A Quick Roadmap 🚀
AI can be overwhelming, but following a structured path makes it easier. Here’s the roadmap:
1. Build Strong Foundations Learn Python, data structures, linear algebra, statistics & version control before diving into AI.
2. Work with Data Clean, preprocess & visualize datasets using Pandas, Seaborn, and Matplotlib for hands-on experience.
3. Master Machine Learning Understand supervised & unsupervised learning, regression, decision trees & implement models with Scikit-Learn.
4. Explore Deep Learning Learn neural networks, CNNs, RNNs, and Transformers using TensorFlow & PyTorch for AI applications.
5. Choose an AI Specialization Focus on NLP, computer vision, reinforcement learning, or AI in business and healthcare.
6. Learn Large Language Models (LLMs) Work with GPT, LLaMA, fine-tuning, Retrieval-Augmented Generation (RAG), and AI APIs.
7. Master AI Deployment & MLOps Deploy models using Flask, FastAPI, Docker, Kubernetes, and automate pipelines.
