Data Collection Internship (AI Dataset Creation For ML Fine-Tuning) Work From Home Part Time Internship

Data Collection Internship (AI Dataset Creation For ML Fine-Tuning)
Start Date
Starts immediatelyImmediately
Duration
1 Month
Stipend
₹ 2,000 lump sum + Incentives
APPLY BY
19 Mar' 25
Posted 3 weeks ago
Internship
Part time

About the work from home job/internship

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.

Skill(s) required

Time Management

Who can apply

Only those candidates can apply who:

1. are available for the work from home job/internship

2. can start the work from home job/internship between 15th Feb'25 and 22nd Mar'25

3. are available for duration of 1 month

4. have relevant skills and interests

* Women wanting to start/restart their career can also apply.

Perks

Certificate Flexible work hours
Additional information

Stipend structure: This is a performance-based internship. In addition to the minimum-assured stipend, you will also be paid a performance-linked incentive (₹ 1 per data entry).

Number of openings

5

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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