Machine learning

About the internship Day-to-day roles and responsibilities for a Machine Learning Developer Intern : 1. Data Collection and Preprocessing: Gather and clean data from various sources to prepare it for model training. 2. Model Development: Assist in building and fine-tuning machine learning models using frameworks like TensorFlow or PyTorch. 3. Algorithm Implementation: Implement and experiment with different algorithms to solve specific problems. 4. Performance Evaluation: Test and evaluate model performance, ensuring accuracy and efficiency. 5. Collaborating with Team Members: Work closely with data scientists, software developers, and other team members to integrate models into products. 6. Research and Experimentation: Stay updated with the latest ML techniques and research, experimenting with new approaches. 7. Documentation: Document processes, model parameters, and findings to maintain a clear record of work. 8. Debugging and Troubleshooting: Identify and fix issues related to data pipelines, model training, or deployment. 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 16th Oct'24 and 20th Nov'24 3. are available for duration of 3 months 4. have relevant skills and interests * Women wanting to start/restart their career can also apply. Qriocity Ventures Private Limited is a cutting-edge edtech company dedicated to elevating students' academic and professional pursuits. With a core focus on projects, courses, and training, Qriocity offers a comprehensive platform where students can access a vast library of project templates, step-by-step guides, and interactive learning modules. Each resource is crafted by industry experts and educators to ensure content relevance and pedagogical effectiveness. For students seeking hands-on training, Qriocity's immersive workshops and virtual labs provide real-world applications, bridging the gap between theory and practice. The company's adaptive learning algorithms personalize the user experience, making it tailor-fit for every learner's unique needs.