Data Collection Internship Ai Dataset Creation For Ml Fine Tuning Work From Home /internship At Billion Hearts

Billion Hearts - Experience : Freshers

Latest Billion Hearts jobs for data collection internship ai dataset creation for ml fine tuning work from home /internship at billion hearts in india. Billion Hearts jobs for as data collection internship ai dataset creation for ml fine tuning work from home /internship at billion hearts.

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Job Information


Job Title : Data Collection Internship Ai Dataset Creation For Ml Fine Tuning Work From Home Job/internship At Billion Hearts
Company Name : Billion Hearts
Job Location : India
Education : Any Graduate / Post Graduate
Category : Internship
Experience : Freshers
Post Date : 17 February, 2025
Last Date : 29 July, 2025
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Job Details
Selected intern’s day-to-day responsibilities include:



1. Manual data collection:

- Browse specified Instagram celebrity accounts.

- Take 10 screenshots per celebrity (posts or profile images).

- Take one blank screenshot of a black screen as a separator after completing each set of 10 screenshots.



2. Organized submission:

- Upload the collected screenshots (with separators) to a shared cloud storage system in the provided folder structure.



3. Progress tracking:

- Update regularly the team on completed tasks and follow project timelines.

Candidate Profile

  1. are Available For The Work From Home Job/internship (it May Be Part Time In-office Or Part Time At Home/work From Home Online)
  2. have Relevant Skills And Interests
  3. can Start The Work From Home Job/internship Between 15th Feb'25 And 22nd Mar'25
  4. are Available For Duration Of 1 Month
  5. have Already Graduated Or Are Currently In Any Year Of Study
  6. females Willing To Start/restart Their Career May Also Apply

Education : Any Graduate / Post Graduate

About Billion Hearts
Looking to fine-tune an ML modelI am currently working on developing a machine learning model, focusing on refining its accuracy and performance through a comprehensive fine-tuning process. To enhance the model's capabilities, I am gathering relevant data, ensuring it represents diverse scenarios and edge cases. By continuously collecting high-quality data, I aim to improve the model's generalization ability, making it more robust and reliable in real-world applications. The fine-tuning process involves iterating over multiple training phases, adjusting parameters, and evaluating its performance on validation sets to ensure it delivers optimal results. Through this iterative approach, I plan to build a model that performs well across various conditions, ultimately achieving a high level of accuracy.
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