Artificial Intelligence
Open in Telegram
🔰 Machine Learning & Artificial Intelligence Free Resources 🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data
Show more
Network:📈 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 56 599 subscribers, ranking 3 072 in the Education category and 5 950 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 56 599 subscribers.
According to the latest data from 30 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 455 over the last 30 days and by 19 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.97%. Within the first 24 hours after publication, content typically collects 1.36% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 246 views. Within the first day, a publication typically gains 770 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 12.
- 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 31 October, 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.
56 599
Subscribers
+1924 hours
+817 days
+45530 days
Posts Archive
56 595
𝗚𝗼𝗼𝗴𝗹𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
Master AI for FREE: 5 Must-Take Google Courses to Boost Your Career
🌟 Artificial Intelligence is transforming industries, and now’s your chance to dive into this exciting field with free, expert-led courses by Google.
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/428e55o
Enroll Now & Get Certfied 🎓
56 595
Advanced AI and Data Science Interview Questions
1. Explain the concept of Generative Adversarial Networks (GANs). How do they work, and what are some of their applications?
2. What is the Curse of Dimensionality? How does it affect machine learning models, and what techniques can be used to mitigate its impact?
3. Describe the process of hyperparameter tuning in deep learning. What are some strategies you can use to optimize hyperparameters?
4. How does a Transformer architecture differ from traditional RNNs and LSTMs? Why has it become so popular in natural language processing (NLP)?
5. What is the difference between L1 and L2 regularization, and in what scenarios would you prefer one over the other?
6. Explain the concept of transfer learning. How can pre-trained models be used in a new but related task?
7. Discuss the importance of explainability in AI models. How do methods like LIME or SHAP contribute to model interpretability?
8. What are the differences between Reinforcement Learning (RL) and Supervised Learning? Can you provide an example where RL would be more appropriate?
9. How do you handle imbalanced datasets in a classification problem? Discuss techniques like SMOTE, ADASYN, or cost-sensitive learning.
10. What is Bayesian Optimization, and how does it compare to grid search or random search for hyperparameter tuning?
11. Describe the steps involved in developing a recommendation system. What algorithms might you use, and how would you evaluate its performance?
12. Can you explain the concept of autoencoders? How are they used for tasks such as dimensionality reduction or anomaly detection?
13. What are adversarial examples in the context of machine learning models? How can they be used to fool models, and what can be done to defend against them?
14. Discuss the role of attention mechanisms in neural networks. How have they improved performance in tasks like machine translation?
15. What is a variational autoencoder (VAE)? How does it differ from a standard autoencoder, and what are its benefits in generating new data?
Like if you need similar content 😄👍
56 595
❗️ Missed TRUMP and FPIBANK? Don't make that mistake again!
You could have turned $50 into $90,000.....
🔥 Don't make that mistake again - Lisa has already found the next memcoin!
The price is still pennies - but in a week the advertising campaign for this token starts.
Lisa gives regular signals in her channel. Now is the last chance to get in before the pampa!
⏳ You either enter the market or wait for the next opportunity.
👉 CLICK HERE TO JOIN LISA'S CHANNEL 👈
56 595
𝟱 𝗙𝗿𝗲𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍
Want to gain real-world experience and make your resume stand out?
These 100% free & remote virtual internships will help you develop in-demand skills from top global companies!
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4bajU4J
Enroll Now & Get Certfied 🎓
56 595
8 FREE AI Courses by Google 🎓🚀 Learn, Grow, and Succeed
1. Introduction to Generative AI
→ An introductory course to explain what generative AI is.
→ You'll learn how AI is used and how it's different from machine learning.
🔗 Course Link
2. Image Generation
→ Discover how to train and deploy a model to generate images.
→ After completing this course, you will be awarded a badge.
🔗 Course Link
3. Responsible AI
→ It explains what responsible AI is and why it's important.
→ Learn the 7 AI principles.
🔗 Course Link
4. Large Language Models
→ Explore what large language models (LLM) are.
→ How you can use prompting tuning to enhance LLM performance.
🔗 Course Link
5. Transformer and BERT Models
→ Two essential AI models.
→ How it is to build the BERT model.
→ Upon completion, you will be awarded a badge.
🔗 Course Link
6. Attention Mechanism
→ Introduce you to the attention mechanism.
→ Find out how it can be applied to enhance AI tasks' performance.
🔗 Course Link
7. Generative AI Studio
→ Integrate AI into your apps.
→ Find out about Generative AI Studio, what it can do, and it's features.
🔗 Course Link
8. Image recognition
→ Learn how to create an AI that understands images.
→ Practical learning so that you can create your own by the end of the course.
🔗 Course Link
All the best 👍👍
#freecourses
56 595
𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗧𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍
1️⃣ BCG Data Science & Analytics
2️⃣ TATA Data Visualization Internship
3️⃣ Accenture Data Analytics
4️⃣ PwC Power BI Internship
5️⃣ British Airways Data Science
6️⃣ Quantium Data Analytics
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4i9L0LA
Enroll For FREE & Get Certified 🎓
56 595
𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
- SQL
- Blockchain
- HTML & CSS
- Excel, and
- Generative AI
These free full courses will take you from beginner to expert!
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4gRuzlV
Enroll For FREE & Get Certified 🎓
56 595
Underrated Telegram Channel for Data Analysts 👇👇
https://t.me/sqlspecialist
Here, you will get free tutorials to learn SQL, Python, Power BI, Excel and many more
Hope you guys will like it 😄
56 595
To automate your daily tasks using ChatGPT, you can follow these steps:
1. Identify Repetitive Tasks: Make a list of tasks that you perform regularly and that can potentially be automated.
2. Create ChatGPT Scripts: Use ChatGPT to create scripts or workflows for automating these tasks. You can use the API to interact with ChatGPT programmatically.
3. Integrate with Other Tools: Integrate ChatGPT with other tools and services that you use to streamline your workflow. For example, you can connect ChatGPT with task management tools, calendar apps, or communication platforms.
4. Set up Triggers: Set up triggers that will initiate the automated tasks based on certain conditions or events. This could be a specific time of day, a keyword in a message, or any other criteria you define.
5. Test and Iterate: Test your automated workflows to ensure they work as expected. Make adjustments as needed to improve efficiency and accuracy.
6. Monitor Performance: Keep an eye on how well your automated tasks are performing and make adjustments as necessary to optimize their efficiency.
56 595
𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗙𝗥𝗘𝗘 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗩𝗶𝗱𝗲𝗼𝘀!😍
Want to become a Data Analytics pro?🔥
These tutorials simplify complex topics into easy-to-follow lessons✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4k5x6vx
No more excuses—just pure learning!✅️
56 595
+3
🔗 Master 8 Essential Machine Learning Algorithms
To truly master these foundational algorithms
It's crucial to dive deeper into their real-world applications and understand how AI is shaping the future.That's where "The Most Effective Guide to Master AI" comes in! This comprehensive guide covers everything you need to know: - Real-world AI applications - Computer Vision - Generative Models - Essential AI tools
