About the program

The Machine Learning Intern at Academy Innova World will work on realworld, impactdriven projects focused on applying AI and ML techniques to education, research, and industryoriented problem statements. The intern will assist in data collection, preprocessing, model development, training, and evaluation using modern ML frameworks. They will collaborate with mentors and interdisciplinary teams to build predictive models, automation solutions, and intelligent systems, while gaining handson exposure to research methodologies and ethical AI practices. This internship emphasizes learning by doing, innovation, and translating theoretical knowledge into practical applications. Skill Set Required: Basic to intermediate knowledge of Python Understanding of machine learning algorithms supervised unsupervised Familiarity with libraries such as NumPy, Pandas, Scikitlearn, TensorFlow or PyTorch Basic data analysis and visualization skills Knowledge of statistics and linear algebra Problemsolving mindset and willingness to learn Good communication and teamwork skills

Perks

Hands-on experience with real-world machine learning and AI projects Mentorship from experienced researchers and industry professionals Opportunity to work on research-oriented and innovation-driven initiatives Internship certificate and letter of recommendation based on performance Skill enhancement through exposure to modern tools, frameworks, and collaborative learning environments

Who can apply?

Only those candidates can apply who:

1. are from B.Tech. / B.E./ Equivalent
2. and specialisation from Artifical Intelligence and Machine Learning,Artificial Intelligence and Data Science,Computer and information Science,Computer Application and Business Management,Computer Applications,Computer Engineering,Computer Engineering and Application,Computer Science
3. are available for duration of 3 Months
4. have relevant skills and interests

Terms of Engagement

Flexible engagement model suitable for students and working professionals Internship duration of 3 months Flexible working hours with remote/online mode Task-based assignments with milestone-driven evaluation

Number of openings

10