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Artificial Intelligence
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๐ฐ Machine Learning & Artificial Intelligence Free Resources ๐ฐ Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data
Show more๐ 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 53 216 subscribers, ranking 3 245 in the Education category and 7 023 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 53 216 subscribers.
According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 1 051 over the last 30 days and by 27 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.06%. Within the first 24 hours after publication, content typically collects 1.66% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 222 views. Within the first day, a publication typically gains 884 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 10.
- Thematic interests: Content is focused on key topics such as learning, classification, layer, pattern, chatbot.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โ๐ฐ Machine Learning & Artificial Intelligence Free Resources
๐ฐ Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_dataโ
Thanks to the high frequency of updates (latest data received on 12 June, 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.
53 216
Subscribers
+2724 hours
+1677 days
+1 05130 days
Posts Archive
53 216
Who's here?
We've asked for a free link to a paid channel, for our subs.
x2-x3 Signals here
๐ CLICK HERE TO JOIN ๐
๐ CLICK HERE TO JOIN ๐
๐ CLICK HERE TO JOIN ๐
โ๏ธJOIN 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.
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๐คฉ Want to build AI Apps and get jobs in GenAI domain? ๐
"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
๐๏ธ : 20th April || 09 PM
In just one hour, you will learn: ๐
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Working with Gemini Models
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Creating Custom Prompts
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Exporting Your App to Code
Register Here: https://tally.so/r/wzKEY8?utm=telegram
Only a few seats left โ ๏ธ
53 216
Who's here?
We've asked for a free link to a paid channel, for our subs.
x2-x3 Signals here
๐ CLICK HERE TO JOIN ๐
๐ CLICK HERE TO JOIN ๐
๐ CLICK HERE TO JOIN ๐
โ๏ธJOIN FAST! FIRST 1000 SUBS WILL BE ACCEPTED
53 216
Complete Roadmap to learn Generative AI in 2 months ๐๐
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 ๐๐
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 LEARNING๐๐
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Best Resource to Learn Artificial Intelligence (AI) For Free
๐๐
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 ๐๐
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How do you start AI and ML ?
Where do you go to learn these skills? What courses are the best?
Thereโs no best answer๐ฅบ. Everyoneโs path will be different. Some people learn better with books, others learn better through videos.
Whatโs 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, youโve 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 Iโve tried every week new course lauch better than others its difficult to recommend any course
Iโve 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
Theyโre all world class. Iโm a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If youโre an absolute beginner, start with some introductory Python courses and when youโre a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.me/machinelearning_deeplearning
๐Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5
Like for more โค๏ธ
All the best ๐๐
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If I were to start Computer Science in 2023,
- Harvard - Stanford
- MIT - IBM - Telegram
- Microsoft - Google
โฏ CS50 from Harvard
http://cs50.harvard.edu/x/2023/certificate/
โฏ C/C++
http://ocw.mit.edu/courses/6-s096-effective-programming-in-c-and-c-january-iap-2014/
โฏ Python
http://cs50.harvard.edu/python/2022/
https://t.me/dsabooks
โฏ SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql
https://t.me/sqlanalyst
โฏ DSA
http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/
https://t.me/crackingthecodinginterview/290
โฏ Java
http://learn.microsoft.com/shows/java-for-beginners/
https://t.me/Java_Programming_Notes
โฏ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/
https://t.me/javascript_courses
โฏ TypeScript
http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
โฏ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
โฏ Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum
โฏ Data Science
cognitiveclass.ai/courses/data-science-101
https://t.me/datasciencefun/1141
โฏ Machine Learning
http://developers.google.com/machine-learning/crash-course
โฏ Deep Learning
introtodeeplearning.com
t.me/machinelearning_deeplearning/
โฏ Full Stack Web (HTML/CSS)
pll.harvard.edu/course/cs50s-web-programming-python-and-javascript/2023-05
t.me/webdevcoursefree/594
โฏ OS, Networking
ocw.mit.edu/courses/6-033-computer-system-engineering-spring-2018/
โฏ Compiler Design
online.stanford.edu/courses/soe-ycscs1-compilers
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
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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:
๐๐
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
โ Melted steel + liquid = ?
โ What if an atomic bomb is detonated in the Mariana Trench?
โ Attempts to drown an anvil in mercury
Subscribe ๐ Physics on fingers
Subscribe ๐ Physics on fingers
Subscribe ๐ Physics on fingers
