Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Artificial Intelligence
Kanalga Telegram’da o‘tish
🔰 Machine Learning & Artificial Intelligence Free Resources 🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data
Ko'proq ko'rsatish📈 Telegram kanali Artificial Intelligence analitikasi
Artificial Intelligence (@machinelearning_deeplearning) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 53 216 obunachidan iborat bo'lib, Taʼlim toifasida 3 245-o'rinni va Hindiston mintaqasida 7 023-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 53 216 obunachiga ega bo‘ldi.
11 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 1 051 ga, so‘nggi 24 soatda esa 27 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 6.06% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.66% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 3 222 marta ko‘riladi; birinchi sutkada odatda 884 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 10 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, classification, layer, pattern, chatbot kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“🔰 Machine Learning & Artificial Intelligence Free Resources
🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 12 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
53 216
Obunachilar
+2724 soatlar
+1677 kunlar
+1 05130 kunlar
Postlar arxiv
53 216
Who's here?
We've asked for a free link to a paid channel, for our subs.
x2-x3 Signals here
👉 CLICK HERE TO JOIN 👈
👉 CLICK HERE TO JOIN 👈
👉 CLICK HERE TO JOIN 👈
❗️JOIN FAST! FIRST 1000 SUBS WILL BE ACCEPTED
53 216
Do you enjoy reading this channel?
Perhaps you have thought about placing ads on it?
To do this, follow three simple steps:
1) Sign up: https://telega.io/c/machinelearning_deeplearning
2) Top up the balance in a convenient way
3) Create an advertising post
If the topic of your post fits our channel, we will publish it with pleasure.
53 216
🤩 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 Jodhpur 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/wzKEY8?utm=telegram
🗓️ : 20th April || 09 PM
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/wzKEY8?utm=telegram
Only a few seats left ⚠️
53 216
Who's here?
We've asked for a free link to a paid channel, for our subs.
x2-x3 Signals here
👉 CLICK HERE TO JOIN 👈
👉 CLICK HERE TO JOIN 👈
👉 CLICK HERE TO JOIN 👈
❗️JOIN FAST! FIRST 1000 SUBS WILL BE ACCEPTED
53 216
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👍👍
53 216
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 👍👍
53 216
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 👍👍
53 216
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 👍👍
53 216
53 216
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
53 216
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
53 216
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
