VerifiedInternship

Machine Learning Intern

RemoteInternshipRemotePosted 2w ago9 views
Est. Salary
Est. ₹2 LPA – 2.5 LPA
Experience
0 Years
Work mode
Remote
Employment
Internship
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Job description

## About the Role UM IT PRIVATE LIMITED is offering a structured Career Launch Internship Program for students to gain practical experience in machine learning and artificial intelligence. The internship spans 3 months and is fully remote, providing mentorship, guided learning resources, and real‑world project exposure. ## Responsibilities - Collect, clean, and preprocess datasets for ML projects. - Perform exploratory data analysis to uncover patterns. - Assist in feature selection and develop basic supervised/unsupervised models. - Train, evaluate, and improve model performance using appropriate metrics. - Create visualizations to communicate results and document experiments. - Collaborate with team members on practical machine‑learning initiatives. ## Eligibility & Required Skills - Currently pursuing or recently graduated in Computer Science, Data Science, AI, Statistics, Mathematics, or related fields. - Basic knowledge of **Python** and familiarity with libraries such as **Pandas**, **NumPy**, and **Scikit‑learn**. - Understanding of statistics, data analysis, and core machine‑learning concepts (supervised & unsupervised learning). - Strong analytical, problem‑solving, communication, and teamwork abilities. ## Perks & Benefits - Performance‑based stipend up to ₹7,500. - Internship certificate and letter of recommendation. - Swags, goodies, and exposure to real‑world ML projects. - Mentorship from industry professionals and a supportive learning environment. ## Equal Opportunity Statement UM IT PRIVATE LIMITED is committed to fostering an inclusive workplace and encourages applicants from diverse backgrounds to apply.

Required skills

Compensation

Est. Min
₹2 LPA
Est. Max
₹2.5 LPA
Currency
INR

Estimated range — based on ML/Data Engineer with 0-1 years experience (5 similar listings). Actual compensation may vary based on your specific experience, interview performance, and the company’s internal bands.

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