Ask chatgpt to give you non cliche project ideas. Works amazingly well.

Here are some of the Beginner Machine learning Projects to do: Handwriting recognition with neural networks Breast cancer classification House price prediction Stock price prediction Emotion recognition Image recognition

Great initiative. I would suggest you to not stop at model training phase. A Jupyter Notebook is not a portfolio piece; a deployed app is. My suggestion: Start with Kaggle (But be selective): Don't just compete. Look at past competitions, specifically the 'Featured' ones. Read the top-scoring kernels to understand the architecture and feature engineering pipelines. Find Unique Data (The Real World): Once you are comfortable, move away from clean Kaggle datasets. Go to data.gov or similar websites and work with real-world datasets and messy data. Model Serving and MLOps: This is the most important part. None of the above teaches you MLOps. Take your model and wrap it in an API (FastAPI or Flask) or build a simple frontend (Streamlit), and a simple monitoring dashoboard. System Design: Read and practice designing ML systems. You can find plenty of free and paid resources on the Internet.

For AI and Machine Learning student projects, consider a range of ideas tailored to different skill levels and timelines. Beginners could explore simple classification tasks like image recognition of handwritten digits or sentiment analysis of text reviews using pre-built libraries. Intermediate students might tackle projects involving regression, such as predicting housing prices or stock market trends, or delve into clustering algorithms for customer segmentation. More advanced learners could experiment with deep learning techniques, developing models for natural language processing like chatbots or machine translation, or explore generative adversarial networks for image generation or style transfer. Projects should focus on real-world problems, such as developing a disease prediction model, creating a smart home automation system, or building an AI-powered educational tool. These projects should encourage collaboration, offer opportunities for interdisciplinary work, and be achievable within a semester or academic year, fostering a deep and practical understanding of AI and ML concepts.