Artificial Intelligence
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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 55 424 suscriptores, ocupando la posición 3 046 en la categoría Educación y el puesto 6 201 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 55 424 suscriptores.
Según los últimos datos del 31 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 702, y en las últimas 24 horas de 25, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.01%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.37% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 328 visualizaciones. En el primer día suele acumular 757 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 26.
- 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 01 septiembre, 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.
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Suscriptores
+2524 horas
+1537 días
+70230 días
Archivo de publicaciones
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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
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
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Best Resource to Learn Artificial Intelligence (AI) For Free
👇👇
https://imp.i115008.net/qn27PL
https://i.am.ai/roadmap
https://bit.ly/3h97QpE
https://t.me/datasciencefun/1375
http://microsoft.github.io/AI-For-Beginners
https://ai.google/education/
Share with credits: https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
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How do you start AI and ML ?
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
I’ve completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
Coursera - Deep Learning by Andrew Ng
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.me/machinelearning_deeplearning
👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍👍
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If I were to start Computer Science in 2023,
- Harvard - Stanford
- MIT - IBM - Telegram
- Microsoft - Google
❯ CS50 from Harvard
http://cs50.harvard.edu/x/2023/certificate/
❯ C/C++
http://ocw.mit.edu/courses/6-s096-effective-programming-in-c-and-c-january-iap-2014/
❯ Python
http://cs50.harvard.edu/python/2022/
https://t.me/dsabooks
❯ SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql
https://t.me/sqlanalyst
❯ DSA
http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/
https://t.me/crackingthecodinginterview/290
❯ Java
http://learn.microsoft.com/shows/java-for-beginners/
https://t.me/Java_Programming_Notes
❯ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/
https://t.me/javascript_courses
❯ TypeScript
http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
❯ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
❯ Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum
❯ Data Science
cognitiveclass.ai/courses/data-science-101
https://t.me/datasciencefun/1141
❯ Machine Learning
http://developers.google.com/machine-learning/crash-course
❯ Deep Learning
introtodeeplearning.com
t.me/machinelearning_deeplearning/
❯ Full Stack Web (HTML/CSS)
pll.harvard.edu/course/cs50s-web-programming-python-and-javascript/2023-05
t.me/webdevcoursefree/594
❯ OS, Networking
ocw.mit.edu/courses/6-033-computer-system-engineering-spring-2018/
❯ Compiler Design
online.stanford.edu/courses/soe-ycscs1-compilers
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
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If you're into deep learning, then you know that students usually one of the two paths:
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:
👇👇
https://t.me/generativeai_gpt/7
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AI/ML roadmap
Topic: Mathematics
- Subtopic: Linear Algebra
- Vectors, Matrices, Eigenvalues and Eigenvectors
- Subtopic: Calculus
- Differentiation, Integration, Partial Derivatives
- Subtopic: Probability and Statistics
- Probability Theory, Random Variables, Statistical Inference
Topic: Programming
- Subtopic: Python
- Python Basics, Libraries like NumPy, Pandas, Matplotlib
Topic: Machine Learning
- Subtopic: Supervised Learning
- Linear Regression, Logistic Regression, Decision Trees
- Subtopic: Unsupervised Learning
- Clustering, Dimensionality Reduction[1](https://i.am.ai/roadmap)
- Subtopic: Neural Networks and Deep Learning
- Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks
Topic: Specializations
- Subtopic: Natural Language Processing
- Text Preprocessing, Topic Modeling, Word Embeddings
- Subtopic: Computer Vision
- Image Processing, Object Detection, Image Segmentation
- Subtopic: Reinforcement Learning
- Markov Decision Processes, Q-Learning, Policy Gradients
Join for more: https://t.me/machinelearning_deeplearning
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You never got that kind of Physics in school
I'm hooked on this crazy Harvard professor's video experiments. He illustrates interesting and simple Physics
— Melted steel + liquid = ?
— What if an atomic bomb is detonated in the Mariana Trench?
— Attempts to drown an anvil in mercury
Subscribe 👉 Physics on fingers
Subscribe 👉 Physics on fingers
Subscribe 👉 Physics on fingers
55 424
🤩 Want to build AI Apps and get jobs in GenAI domain? 🚀
"How to fine-tune a LLM?" is a 1-hour FREE Materclass by IIT Delhi Alumni to help you dive into the world of fine-tuning large language models.
Register Now: https://www.buildfastwithai.com/events/how-to-fine-tune-a-llm
🗓️ : 14th April || 11 AM
In just one hour, you will learn: 📕
✅ Fundamentals of fine-tuning for AI
✅ Hands-on GPT-3.5 fine-tuning tutorial
✅ Exploring open-source LLM fine-tuning
✅ Q&A and open discussion with experts
Register Here: https://www.buildfastwithai.com/events/how-to-fine-tune-a-llm
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Repost from Data Science & Machine Learning Free Resources
Artificial Intelligence with Python
Teik Toe Teoh, 2022
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55 424
🤩 Want to build AI Apps and get jobs in GenAI domain? 🚀
"Build AI Apps with Google AI Studio!" is a 1-hour FREE Materclass by IIT Delhi Alumni to help you gain valuable insights into building AI applications without coding and make you ready for your next job.
Register Now: https://tally.so/r/mVJgay
🗓️ : 6th April || 11 AM
In just one hour, you will learn: 📕
✅ Working with Gemini Models
✅ Creating Custom Prompts
✅ Exporting Your App to Code
Register Here: https://tally.so/r/mVJgay
Only a few seats left ⚠️
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95% of Machine Learning solutions in the real world are for tabular data.
Not LLMs, not transformers, not agents, not fancy stuff.
Learning to do feature engineering and build tree-based models will open a ton of opportunities.
