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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📈 Analytical overview of Telegram channel Artificial Intelligence
Channel Artificial Intelligence (@machinelearning_deeplearning) in the English language segment is an active participant. Currently, the community unites 56 619 subscribers, ranking 3 064 in the Education category and 5 904 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 56 619 subscribers.
According to the latest data from 01 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 423 over the last 30 days and by 15 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.48%. Within the first 24 hours after publication, content typically collects 1.54% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 534 views. Within the first day, a publication typically gains 874 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 13.
- Thematic interests: Content is focused on key topics such as learning, classification, layer, pattern, chatbot.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“🔰 Machine Learning & Artificial Intelligence Free Resources
🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data”
Thanks to the high frequency of updates (latest data received on 02 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
56 619
Subscribers
+1524 hours
+737 days
+42330 days
Posts Archive
56 625
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
56 625
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.
56 625
🤩 Want to build AI Apps and get jobs in GenAI domain? 🚀
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56 625
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
56 625
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👍👍
56 625
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 👍👍
56 625
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 👍👍
