Having AI projects is crucial when applying for AI jobs, particularly for fresher and less experienced candidates. Properly made projects are solid evidence that the candidate is able to use theoretical knowledge in practice.
Projects will prove that you have knowledge and skills in Python, data preprocessing, machine learning, deep learning, NLP, Generative AI, model evaluation, and deployment. You will also have some concrete examples to talk about during technical interviews.
It might be beneficial to make customer churn prediction, recommendation engine, chatbot, sentiment analysis, or image classification projects in order to show the ability to work with real problems and datasets.
It is better to focus on quality than quantity. Rather than including ten trivial projects to your resume, it is better to focus on 2–4 high-quality projects that prove certain skills. Explain what problem was solved, what dataset was used, what methods were used, which difficulties you had, and the result of your project.
When doing an AI course, try to make projects that are industry-oriented and portfolio-friendly. It is essential to maintain your code on GitHub and describe your work properly. Having a solid project portfolio and proper skills may be crucial for getting shortlisted for entry-level
Know more-
Full Stack Course in BanerFull Stack Classes in BanerFull Stack Training in Baner
Locked
