Vilsi Jain

- Continuous Learning: Stay updated with the latest research papers, attend conferences, and take online courses. - Hands-on Projects: Work on real-world projects to apply your knowledge. This could be through internships, freelance work, or personal projects. - Building a portfolio of projects can showcase your skills to potential employers. - Networking: Join machine learning communities, attend meetups, and participate in online forums like Reddit and Stack Overflow. Networking can open up opportunities for collaboration and job offers. - Contribute to Open Source: Contributing to open-source projects can help you gain experience, learn from others, and build a reputation in the community.

Balancing the need for extensive feature engineering what folks actually - Prioritize Features Based on Impact: Focus on creating features that are most likely to have a significant impact on model performance - Automate Where Possible: Use automated feature engineering tools or libraries that can help generate and evaluate features quickly - Feature Engineering: Set strict time limits for feature engineering phases. If a feature doesn’t show promise within the allotted time - Maintain a report - If it is a bug try to fix it first before launching the feature as whatever you show on site is important to make good results then start work on a new feature which is highly needed and making more profit.

Fostering diversity and inclusion in a machine learning team is crucial for driving innovation and creating equitable solutions. Here are several strategies to promote diversity and inclusion: - Recruit from Diverse Talent Pools: Make sure your company have good ratio of diversity ,don't make a difference between college hiring. - Create an Inclusive Culture: Encourage open communication, where everyone is comfortable sharing ideas and feedback. - Provide Equal Growth Opportunities: Offer training, mentorship, and professional development opportunities equally to all team members - Encourage Diverse Perspectives in Problem-Solving: make hiring through Hackathons and open source work

Managing expectations in machine learning projects with limited resources can be challenging, but here are some effective strategies: - Prioritize High-Impact Tasks: Focus on tasks that deliver the most value with the resources at hand. - Realistic Goals: Align project goals with the resources available. Start from a small project. - Communicate Regularly: Keep stakeholders informed about progress, challenges, and any resource limitations. Regular updates help manage expectations and build trust in the community focus on github. - Leverage Pre-trained Models and Tools: Utilize existing models, frameworks, and tools that can save time and resources. - Collaborate and Seek External Support