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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📈 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 56 619 suscriptores, ocupando la posición 3 064 en la categoría Educación y el puesto 5 904 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 56 619 suscriptores.
Según los últimos datos del 01 noviembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 423, y en las últimas 24 horas de 15, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.48%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.54% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 534 visualizaciones. En el primer día suele acumular 874 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 13.
- 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 02 noviembre, 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.
56 619
Suscriptores
+1524 horas
+737 días
+42330 días
Archivo de publicaciones
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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
56 625
🤩 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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Are you here? I asked for a private link from the admins of this channel:
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56 625
🤩 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.
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𝗚𝗶𝘁 𝗠𝗲𝗿𝗴𝗲 𝘃𝘀 𝗥𝗲𝗯𝗮𝘀𝗲
One of the most powerful Git features is branching. Yet, while working with it, we must integrate changes from one branch into another. The way how to do this can be different.
We have two ways to do it:
𝟭. 𝗠𝗲𝗿𝗴𝗲
When you merge Branch A into Branch B (with 𝚐𝚒𝚝 𝚖𝚎𝚛𝚐𝚎), Git creates a new merge commit. This commit has two parents, one from each branch, symbolizing the confluence of histories. It's a non-destructive operation, preserving the exact history of your project, warts, and all. Merges are particularly useful in collaborative environments where maintaining the integrity and chronological order of changes is essential. Yet, merge commits can clutter the history, making it harder to follow specific lines of development.
𝟮. 𝗥𝗲𝗯𝗮𝘀𝗲
When you rebase Branch A onto Branch B (with 𝚐𝚒𝚝 𝚛𝚎𝚋𝚊𝚜𝚎), you're essentially saying, "Let's pretend these changes from Branch A were made on top of the latest changes in Branch B." Rebase rewrites the project history by creating new commits for each commit in the original branch. This results in a much cleaner, straight-line history. Yet, it could be problematic if multiple people work on the same branch, as rebasing rewrites history, which can be challenging if others have pulled or pushed the original branch.
So, when to use them:
🔹 𝗨𝘀𝗲 𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘁𝗼 𝗽𝗿𝗲𝘀𝗲𝗿𝘃𝗲 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗵𝗶𝘀𝘁𝗼𝗿𝘆, especially on shared branches or for collaborative work. It's ideal for feature branches to merge into a main or develop branch.
🔹 𝗨𝘀𝗲 𝗿𝗲𝗯𝗮𝘀𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗯𝗿𝗮𝗻𝗰𝗵𝗲𝘀 or when you want a clean, linear history for easier tracking of changes. Remember to rebase locally and avoid pushing rebased branches to shared repositories. Also, be aware 𝗻𝗼𝘁 𝘁𝗼 𝗿𝗲𝗯𝗮𝘀𝗲 𝗽𝘂𝗯𝗹𝗶𝗰 𝗵𝗶𝘀𝘁𝗼𝗿𝘆. If your branch is shared with others, rebasing can rewrite history in a way that is disruptive and confusing to your collaborators.
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❌ THE MOST PRIVATE GROUP №1 ❌
They are robbing Crypto Exchanges for Millions of dollars!
Yesterday profit = 50,000$+
👉 https://t.me/+5YRwSjrwkCIwNDE1
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Go fast! Only the first 1000 subs will be accepted! 👀🚀
