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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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 55 413 subscribers, ranking 3 046 in the Education category and 6 201 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 55 413 subscribers.
According to the latest data from 31 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 702 over the last 30 days and by 25 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.01%. Within the first 24 hours after publication, content typically collects 1.37% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 328 views. Within the first day, a publication typically gains 757 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 26.
- 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 01 September, 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.
55 413
Subscribers
+2524 hours
+1537 days
+70230 days
Posts Archive
55 413
Some useful AI tools in 2024
Solves anything -> Gemini
Text to image -> Adobe Firefly
Create AI Avatar -> HeyGen
Create Art -> Midjourney
Video editing -> Topview AI
Text to video -> Pika 1.0
Create logo -> logodiffusion
Create interface -> Uiverse
Creates copycats -> Tome
Essay assistant -> Jenni AI
Repetitive tasks -> Zapier
Copies your voice -> Eleven Labs
Rewrite anything -> Quillbot
Drawing assistant -> Autodraw
Create slide deck -> Autodraw
Write any emails -> Addy AI
Summarize notes -> Wordtune
Create music -> Soundraw
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⚠️ WARNING!
You have been cancelled by the channel administrator.And what if I tell you that there is such a closed telegram channel, where the guy for a percentage of profits, shares with his subscribers different private schemes to earn money? The guy has already bypassed the defence of hundreds of sites and was able to find an opportunity to earn in each of them, if you follow the actions of his instructions from the channel, you can easily make good money right now. Entry is limited and will only be available to the first 100 people who sign up ⏱👇 https://t.me/+zXMMfy8nyh05YWQ0
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Unpopular opinion:
ChatGPT is only as smart as the user; if garbage goes in, garbage comes out.
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Breaking into ML Engineering can be very confusing in 2024!
Should I learn TensorFlow or PyTorch? Python or R? Scikit-learn or XGBoost? GCP or AWS? FastAPI or Streamlit?
Fundamental principles are more important than tools:
- understanding statistics and deep learning is more important than TensorFlow vs PyTorch.
- understanding functional and object-oriented programming is more important than Python or R.
- understanding feature engineering is more important than Scikit-learn vs XGBoost.
- understanding scalable and resilient architectures is more important than GCP or AWS.
- understanding models serving is more important than FastAPI or Streamlit.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
Join for more: https://t.me/machinelearning_deeplearning
All the best 👍👍
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ChatGPT Prompt to learn any skill
👇👇
I am seeking to become an expert professional in [Making ChatGPT prompts perfectly]. I would like ChatGPT to provide me with a complete course on this subject, following the principles of Pareto principle and simulating the complexity, structure, duration, and quality of the information found in a college degree program at a prestigious university. The course should cover the following aspects: Course Duration: The course should be structured as a comprehensive program, spanning a duration equivalent to a full-time college degree program, typically four years. Curriculum Structure: The curriculum should be well-organized and divided into semesters or modules, progressing from beginner to advanced levels of proficiency. Each semester/module should have a logical flow and build upon the previous knowledge. Relevant and Accurate Information: The course should provide all the necessary and up-to-date information required to master the skill or knowledge area. It should cover both theoretical concepts and practical applications. Projects and Assignments: The course should include a series of hands-on projects and assignments that allow me to apply the knowledge gained. These projects should range in complexity, starting from basic exercises and gradually advancing to more challenging real-world applications. Learning Resources: ChatGPT should share a variety of learning resources, including textbooks, research papers, online tutorials, video lectures, practice exams, and any other relevant materials that can enhance the learning experience. Expert Guidance: ChatGPT should provide expert guidance throughout the course, answering questions, providing clarifications, and offering additional insights to deepen understanding. I understand that ChatGPT's responses will be generated based on the information it has been trained on and the knowledge it has up until September 2021. However, I expect the course to be as complete and accurate as possible within these limitations. Please provide the course syllabus, including a breakdown of topics to be covered in each semester/module, recommended learning resources, and any other relevant information
(Tap on above text to copy)
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How to Tailor Resume based on the Job Description 👇
To tailor your resume based on a job description:
1. Keyword Integration: Identify key words in the job description and incorporate them into your resume, especially in the skills and experience sections.
2. Relevant Experience: Highlight experiences that directly relate to the job requirements. Focus on accomplishments and skills relevant to the position.
3. Customize Objective or Summary: Tailor your resume objective or summary to align with the specific job, emphasizing how your skills and experience make you a strong fit.
4. Quantify Achievements: Use quantifiable metrics to showcase your achievements. Numbers stand out and provide concrete evidence of your impact.
5. Matched Skills Section: Create a skills section that mirrors the required skills in the job description. Be truthful, but emphasize the skills most relevant to the role.
6. Reorder Sections: Arrange resume sections to prioritize the most relevant information. If education is crucial, move it up; if experience is paramount, highlight it prominently.
7. Research the Company: Tailor your resume to the company culture and values. Showcase experiences that demonstrate your alignment with their mission.
8. Use Action Verbs: Start bullet points with strong action verbs to convey a sense of accomplishment and capability.
Join @getjobss for latest jobs and internship opportunities
Share with your friends if it helps 😄
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This is a class from Harvard University:
"Introduction to Data Science with Python."
It's free. You should be familiar with Python to take this course.
The course is for beginners. It's for those who want to build a fundamental understanding of machine learning and artificial intelligence.
It covers some of these topics:
• Generalization and overfitting
• Model building, regularization, and evaluation
• Linear and logistic regression models
• k-Nearest Neighbor
• Scikit-Learn, NumPy, Pandas, and Matplotlib
Link: https://pll.harvard.edu/course/introduction-data-science-python
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Deep Learning Course – Math and Applications
👇👇
https://www.freecodecamp.org/news/deep-learning-course-math-and-applications
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
