Here are popular websites & resources that provide machine‑learning project examples, tutorials, datasets, and code you can use or adapt — grouped by what they’re best for:

1. Kaggle

• Large collection of end‑to‑end notebooks, competitions, and datasets. Great for applied projects (classification, NLP, time series, vision) and learning best practices.

2. GitHub (search / curated lists)

• Search repos for project keywords; look for “awesome-machine-learning” and “project-based” lists. Contains full source code, model checkpoints, and deployment examples.

3. Papers with Code

• Links research papers to implementations and leaderboards. Useful for reproducing state‑of‑the‑art models and finding benchmark tasks.

4. Hugging Face

• Pretrained models, example notebooks, demo apps (Transformers, tokenizers, diffusers). Excellent for NLP, multimodal and deployment (Inference API, Spaces).

5. TensorFlow Tutorials / TensorFlow Hub

• Step‑by‑step TF/Keras tutorials and reusable model modules. Good for beginners through advanced (image, text, audio, TFLite, TF Serving).

6. PyTorch Tutorials / TorchHub

• Official PyTorch example projects and model zoo with clear code for research-to-production workflows.

7. fast.ai

• Course notebooks and project suggestions focused on practical deep learning (vision, NLP, tabular). Emphasizes quick iteration and transfer learning.

8. UCI Machine Learning Repository

• Classic datasets and brief descriptions — good for small/Medium projects and algorithm comparison.

9. OpenML

• Searchable dataset repository with experiment sharing and reproducibility tools.

10. DrivenData

• Data‑science competitions with social impact datasets and example kernels — good for project ideas with real problems.

11. Coursera / edX / Udacity project pages

• Many courses include guided capstone projects with templates, datasets, and grading rubrics.

12. Towards Data Science / Medium blogs

• Project walkthroughs and end‑to‑end tutorials (often with GitHub links). Useful for learning workflows and writeups.

13. Model zoos & specialized hubs

• NVIDIA NGC, Google Colab examples, Hugging Face Spaces, and Paperspace Gradient provide runnable demos and notebooks for deployment experiments.

How to pick projects:

• Beginner: image classification (CIFAR/MNIST), sentiment analysis (IMDB), tabular churn prediction.
• Intermediate: object detection (COCO), sequence‑to‑sequence (translation), time‑series forecasting.
• Advanced: large‑scale NLP finetuning, multimodal models, production pipelines (ETL → model → API → monitoring).

If you want, I can:

• Suggest 5 project ideas tailored to your skill level and interests (vision / NLP / tabular / deployment).
• Provide starter GitHub repos or a one‑page checklist for turning a notebook into a deployable app.