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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

Network:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India5 954 Education3 067...

📈 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 56 610 subscribers, ranking 3 067 in the Education category and 5 954 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 56 610 subscribers.

According to the latest data from 31 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 437 over the last 30 days and by 3 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 4.10%. 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 2 323 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 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 01 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.

56 610

Subscribers

+324 hours

+647 days

+43730 days

2 323

Post views

~ 87424 hours

~ 1 02448 hours

4.10%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

56 610

10. Explaining Career Transitions: How can I effectively explain a career transition to [JOB TITLE] in [SPECIFIC INDUSTRY] during an interview? Given my background in [previous industry or role] and my recent [relevant education, certification, experience], provide a narrative that connects my previous experiences to the new role, highlighting transferable skills and relevant achievements. ChatGPT PROMPTS Series

56 610

9. Dealing with Gaps in Employment: How should I address gaps in my employment history during an interview for the [JOB TITLE] position in [SPECIFIC INDUSTRY]? Considering that during this period I [explain what you did: pursued education, volunteered, freelanced, etc.], provide a response that explains the gaps positively and focuses on what I’ve learned during that time.

56 610

8. Handling Behavioral Questions: How can I best respond to behavioral interview questions for a [JOB TITLE] role? Given my experience in [specific past role or project], provide strategies and examples to answer questions about teamwork, conflict resolution, and leadership.

56 610

7. Dealing with Gaps in Employment: What is the best way to follow up after an interview for the [JOB TITLE] role at [SPECIFIC COMPANY]? Considering our discussion on [specific topics discussed during the interview], craft a professional and thoughtful thank-you email that reiterates my interest, highlights key points from our conversation, and emphasizes how my background in [specific skills or experiences] aligns with the company’s needs.

56 610

6. Showcasing Soft Skills: How can I effectively highlight my soft skills, such as communication and teamwork, during an interview for the [JOB TITLE] role in [SPECIFIC INDUSTRY]? Please provide examples and scenarios that demonstrate these skills in action.

56 610

5. Post-Interview Follow-Up: What is the best way to follow up after an interview for the [JOB TITLE] role at [SPECIFIC COMPANY]? Based on our discussion about [specific project, skill, or topic discussed during the interview], draft a professional and personalized thank-you email that not only reiterates my enthusiasm for the role but also highlights how my experience in [specific relevant experience or achievement] can directly contribute to the success of [SPECIFIC COMPANY]

56 610

4. Negotiating Salary: How should I approach salary negotiations for a [JOB TITLE] role at [SPECIFIC COMPANY]? Please provide a script or key points to emphasize based on industry standards and my qualifications. ChatGPT PROMPTS Series

56 610

3. Overcoming Weaknesses: How should I address the common interview question: 'What is your greatest weakness?' in the context of a [JOB TITLE] role in [SPECIFIC INDUSTRY]? Provide a response that turns the weakness into a positive aspect.

56 610

2. Mock Interview Practice: Create a mock interview scenario for the [JOB TITLE] role at [SPECIFIC COMPANY]. Include 5 common and challenging questions I might face, and provide guidance on how to answer each effectively.

56 610

1. Developing STAR Method Responses: Help me craft a STAR (Situation, Task, Action, Result) response to the interview question: [INSERT QUESTION] for the [JOB TITLE] role. Ensure the response is clear, concise, and demonstrates my impact in previous roles.

56 610

Here are 10 ChatGPT-4o Prompts you need to know to Dominate and Excel at any job interview:

56 610

#meme

56 610

Data Science Essentials in Python.pdf5.01 MB

56 610

8. Set up the user interface and trigger the main function. • Provides an input field for the user's question • Triggers the main function when the user clicks "Get Answer"

56 610

7. Define the main function to run all LLMs and aggregate results. • Runs all reference models asynchronously • Displays individual responses in expandable sections • Aggregates responses using the aggregator model • Streams the aggregated response.

56 610

6. Implement the LLM call function. • Asynchronously calls the LLM with the user's prompt • Returns the model name and its response

56 610

5. Define the models and aggregator system prompt. • Specifies the LLMs to be used for generating responses • Defines the aggregator model and its system prompt

56 610

4. Initialize Together AI clients. • Sets up Together API key as an environment variable • Initializes both synchronous and asynchronous Together clients

56 610

3. Set up the Streamlit app and API key input. • Creates a title for the app • Adds a secure input field for the Together API key