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
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Red:📈 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 55 377 suscriptores, ocupando la posición 3 050 en la categoría Educación y el puesto 6 211 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 55 377 suscriptores.
Según los últimos datos del 30 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 683, y en las últimas 24 horas de 41, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.87%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.33% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 250 visualizaciones. En el primer día suele acumular 736 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 25.
- 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 31 agosto, 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.
55 377
Suscriptores
+4124 horas
+1517 días
+68330 días
Archivo de publicaciones
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𝟑 𝐒𝐤𝐢𝐥𝐥𝐬 𝐓𝐨 𝐆𝐞𝐭 𝐚 𝗛𝗶𝗴𝗵 𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯 𝐉𝐨𝐛 𝐈𝐧 𝟐𝟎𝟐𝟓 😍
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55 377
Repost from Old Glory Vortex
Trump is not even in the White House yet, and the United States is already being clowned on (unmute for full experience)
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55 377
Essential Programming Languages to Learn Data Science 👇👇
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts 👇👇
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𝗧𝗼𝗽 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗶𝗻 𝗧𝗲𝗰𝗵 𝗮𝗻𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴!😍
Here’s a list of amazing courses that can give your tech career the boost it needs!
From AI applications and data engineering to management strategies and DevOps projects, these courses provide practical knowledge and valuable insights for all skill levels
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55 377
How to master ChatGPT-4o....
The secret? Prompt engineering.
These 9 frameworks will help you!
APE
↳ Action, Purpose, Expectation
Action: Define the job or activity.
Purpose: Discuss the goal.
Expectation: State the desired outcome.
RACE
↳ Role, Action, Context, Expectation
Role: Specify ChatGPT's role.
Action: Detail the necessary action.
Context: Provide situational details.
Expectation: Describe the expected outcome.
COAST
↳ Context, Objective, Actions, Scenario, Task
Context: Set the stage.
Objective: Describe the goal.
Actions: Explain needed steps.
Scenario: Describe the situation.
Task: Outline the task.
TAG
↳ Task, Action, Goal
Task: Define the task.
Action: Describe the steps.
Goal: Explain the end goal.
RISE
↳ Role, Input, Steps, Expectation
Role: Specify ChatGPT's role.
Input: Provide necessary information.
Steps: Detail the steps.
Expectation: Describe the result.
TRACE
↳ Task, Request, Action, Context, Example
Task: Define the task.
Request: Describe the need.
Action: State the required action.
Context: Provide the situation.
Example: Illustrate with an example.
ERA
↳ Expectation, Role, Action
Expectation: Describe the desired result.
Role: Specify ChatGPT's role.
Action: Specify needed actions.
CARE
↳ Context, Action, Result, Example
Context: Set the stage.
Action: Describe the task.
Result: Describe the outcome.
Example: Give an illustration.
ROSES
↳ Role, Objective, Scenario, Expected Solution, Steps
Role: Specify ChatGPT's role.
Objective: State the goal or aim.
Scenario: Describe the situation.
Expected Solution: Define the outcome.
Steps: Ask for necessary actions to reach solution.
Join for more: https://t.me/machinelearning_deeplearning
55 377
𝐆𝐨𝐨𝐠𝐥𝐞 𝐅𝐑𝐄𝐄 𝐀𝐈/𝐌𝐋 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞😍
Unlock the world of AI/ML with Google’s completely free course series!
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55 377
Complete Roadmap to learn Generative AI in 2 months 👇👇
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI 👇👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
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Repost from Trump's Ear
The world stands on a precipice, and the trends are not good. It was the United States whose economic wherewithal rescued Western civilisation in the Second World War and the Cold War. But whereas in 1945 the US accounted for half of all global manufacturing, it now accounts for roughly 16 per cent. America’s gross national debt stands at $36trn, and that debt is growing by $1trn annually.
#US #Trump #Europe
👂 More on Trump's Ear
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Python Facial Recognition System Roadmap
Stage 1: Learn Python basics and OpenCV library.
Stage 2: Preprocess images with Gaussian blur.
Stage 3: Implement face detection using Haar cascades or DNNs.
Stage 4: Extract facial embeddings using libraries like dlib or FaceNet.
Stage 5: Train a classifier (SVM/KNN) for recognition.
Stage 6: Test on datasets.
Stage 7: Build a GUI with Tkinter or PyQt.
Stage 8: Deploy using Flask or FastAPI.
🏆 – Python Facial Recognition System.
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𝐀𝐦𝐚𝐳𝐨𝐧 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍
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𝐍𝐕𝐈𝐃𝐈𝐀 𝐅𝐑𝐄𝐄 𝐀𝐈 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍
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55 377
Here's a simple but powerful test to see the intelligence of an AI model. (The answer is the strawberry is still on the table)
Go ahead and ask any model this:
Assume the laws of physics on Earth. A small
strawberry is put into a normal cup and the cup is
placed upside down on a table. Someone then takes
the cup and puts it inside the microwave. Where is the
strawberry now? Explain your reasoning step by step.
