Great — here are many places and practical ideas to find AI projects you can use to practice, plus tips on picking and completing them.

Where to find ready projects, datasets, and challenges

• GitHub — search topics like "machine-learning", "deep-learning", "computer-vision", "nlp", "awesome-ml" and explore repos with "good first issue" labels to contribute.
• Kaggle — competitions, kernels (notebooks), and a huge dataset catalog for supervised, unsupervised, and end-to-end projects.
• Papers with Code — links research papers to code and implementations grouped by task (e.g., object detection, language modeling) and provides leaderboards and datasets.
• Hugging Face Hub — models, datasets, example notebooks and lots of community projects for NLP, vision, speech, and multimodal work.
• GitLab / Bitbucket — alternative places to find project repos and open-source teams.
• Colab & Kaggle Notebooks — shared notebooks you can fork and run immediately in the cloud.
• Competitions & Challenges — DrivenData, Zindi, AIcrowd, and Kaggle for real problems and deadlines.
• University capstones & MOOCs — course project lists and capstone repositories (e.g., fast.ai course projects, DeepLearning.AI capstones).
• Open data catalogs — UCI Machine Learning Repository, AWS Public Datasets, Google Dataset Search for raw data.
• "Awesome" curated lists — search “awesome machine learning” or “awesome deep learning” for curated project lists and tutorials.

Project ideas by level (quick starters to portfolio-level)

• Beginner
  • Binary classifier on a tabular dataset (credit default, diabetes).
  • Image classifier (CIFAR-10, MNIST) with a simple CNN.
  • Sentiment analysis on movie reviews using an LSTM or transformer.
• Intermediate
  • Object detection on COCO subset or custom dataset (YOLO/Detectron2).
  • Time-series forecasting (electricity usage, stock prices) with LSTM/Transformer + baseline models.
  • Recommendation system using implicit feedback (matrix factorization, LightFM).
• Advanced / Portfolio
  • End-to-end deployed app: fine-tune a transformer for Q&A or summarization, build a web UI (Gradio/Streamlit) and Dockerize it.
  • Multimodal project: combine image + text inputs for classification or retrieval.
  • Research replication: reproduce and extend a recent paper from Papers with Code, produce reproducible notebooks and metrics.
  • Production ML: build a pipeline (data validation, training, monitoring) with MLflow or TFX.

How to pick a good practice project

• Learn a new concept end-to-end: data collection/cleaning → modeling → evaluation → deployment.
• Start small and iterate: baseline simple model first, then add complexity and ablation studies.
• Pick something meaningful or interesting to you (domain knowledge helps).
• Prefer projects with publicly available datasets and clear evaluation metrics.

Helpful tools and platforms for building & deploying

• Notebooks: Google Colab, Kaggle Notebooks, Jupyter.
• Frameworks: scikit-learn (baselines), PyTorch, TensorFlow/Keras, Hugging Face Transformers, Detectron2.
• Datasets: Kaggle Datasets, Hugging Face Datasets, UCI, Open Images, COCO.
• Deployment & demo: Gradio, Streamlit, Flask/FastAPI + Docker, Heroku, Vercel, AWS/GCP/Azure for production.
• Experiment tracking: Weights & Biases, MLflow, TensorBoard.

How to build a portfolio that stands out

• Public repo with clear README, instructions to run, and license.
• Notebook or short demo video/screenshots showing results and interface.
• Write a brief blog post or README explaining approach, dataset, preprocessing, metrics, and lessons learned.
• Add tests or CI, and make it easy for others to reproduce results (requirements.txt / environment file, seed control).

Ways to get ongoing practice and feedback

• Join Kaggle competitions or community kernels and copy/modify top notebooks.
• Contribute to open-source ML projects (small bug fixes, docs, examples).
• Participate in meetups, study groups, or ML Discord/Slack communities.
• Do code reviews or pair programming with peers.

If you want, I can:

• Suggest 3 project ideas tailored to your skill level and interests (state your level and what areas you like: NLP, vision, tabular, etc.).
• Find specific datasets and starter notebooks for any project you choose.

Which would you like next?