Great — here’s a practical, actionable guide to finding and doing real-world AI projects for practice, building skills, and creating a portfolio employers will notice.

1. Decide your focus and level

• Pick a subfield (computer vision, NLP, time series/forecasting, recommender systems, tabular ML, RL, MLOps).
• Be realistic about scope: aim for 1–3 small-to-medium projects (1–6 weeks each) that show end-to-end work.

2. Best places to find project prompts & competitions

• Kaggle — datasets + competitions (good for structured problems and notebooks).
• DrivenData, Zindi — socially impactful competitions and region-specific problems.
• CodaLab — academic-style challenges.
• Hugging Face — community tasks, model hub, hosted datasets and spaces.
• Papers with Code — reproductions and tasks tied to recent papers (good for research-to-application projects).
• GitHub Issues / Good First Issues — search for issues tagged “good first issue” in ML repos.
• Open-source orgs (e.g., scikit-learn, spaCy, Transformers) — contribute to docs, examples, model improvements.
• Local hackathons / meetups / ML/AI Slack or Discord communities — teams and real constraints.
• Government and open data portals (data.gov, NYC Open Data, EU Open Data) — real-world datasets.
• Freelance platforms (Upwork, Fiverr) and volunteer platforms (DataKind, Catchafire) — short paid/volunteer projects.

3. Sources of real-world datasets

• Kaggle Datasets, Hugging Face Datasets, UCI Machine Learning Repository.
• Google Dataset Search.
• AWS/Open Data Registry, Microsoft Azure Open Datasets, Google Cloud Public Datasets.
• Domain-specific: PhysioNet (health), OpenImages/COCO (vision), Common Voice / Librispeech (speech), NOAA (weather), SEC EDGAR (finance), OpenStreetMap (geospatial).
  When you pick a dataset, look for rawness: missing values, messy labels, temporal splits — these simulate real-world challenges.

4. Project types that simulate real-world work

• End-to-end pipeline: data ingestion → cleaning → EDA → modeling → validation → deployment.
• Model productionization: containerize a model, create an API (FastAPI/Flask), CI/CD, monitoring (Prometheus/Grafana).
• Data labeling & human-in-the-loop: build a small labeling tool + active learning loop.
• Model interpretability & fairness audit: bias metrics, feature importance, counterfactuals.
• Scalability / performance: optimize inference latency, quantization, batching.
• MLOps: automated training pipelines (Airflow/Prefect), reproducibility (MLflow/DVC), reproducible environments (Docker).
• Research reproduction: reimplement a recent paper and compare results.

5. Concrete starter project ideas (with minimal guidance)

• Customer churn prediction (tabular): dataset from telecom or Kaggle. Task: build features, handle class imbalance, explain predictions, deploy a REST API. Tools: Python, scikit-learn, SHAP, Flask, Docker.
• Image classification for noisy labels: use CIFAR or OpenImages subset; simulate label noise, implement robust training and augmentation, measure calibration. Tools: PyTorch, torchvision, Albumentations.
• Sentiment analysis on product reviews (NLP): clean text, fine-tune transformer (Hugging Face), add explainability (LIME/SHAP).
• Time-series forecasting for demand or energy: use M4 or electricity consumption dataset. Task: baseline + advanced model (Prophet, LSTM, Transformer), backtest with proper time splits.
• Object detection for a domain (retail, drones): use COCO or create small labeled set; train YOLOv8 / Detectron2; evaluate mAP and build a demo.
• Recommender: collaborative filtering + content features; offline metrics and a simple online A/B test simulation.

6. How to choose and scope a project

• Use the “vertical slice” approach: build the smallest useful end-to-end version first (proof-of-concept), then iterate.
• Focus on one strong contribution: better features, cleaner evaluation, deployment, or explainability.
• Timebox: 1–2 weeks for a small project, 4–6 weeks for something deeper.

7. Build an end-to-end portfolio entry (what to show)

• Clear problem statement and impact (who benefits, metric of success).
• Data description and preprocessing steps (challenges you solved).
• Model(s) tried and why, hyperparameter tuning approach.
• Evaluation with realistic splits (temporal if relevant) and baseline comparisons.
• Interpretability/failure analysis (what went wrong and why).
• Deployment demo (simple web app or notebook + instructions).
• Reproducibility: share code, Dockerfile or environment.yml, data links, and a README with exact steps.
• Small write-up or blog post that explains tradeoffs and decisions.

8. Tools & stack recommendations

• Code + experiments: Python, Jupyter/Colab, PyTorch or TensorFlow, scikit-learn.
• Datasets & models: Hugging Face, Kaggle, Papers with Code.
• Experiment tracking: MLflow, Weights & Biases (free tier).
• Versioning: Git + GitHub; data versioning: DVC or Git-LFS for small teams.
• Deployment: Docker, FastAPI/Flask, Streamlit or Gradio for demos; cloud: Heroku / Render / Vercel / AWS / GCP.
• CI/CD & pipeline orchestration: GitHub Actions, GitLab CI, Airflow, Prefect.

9. Ways to get feedback & mentorship

• Share work on GitHub and write a short blog post (Medium, dev.to, LinkedIn).
• Request code reviews in relevant GitHub communities or on r/MachineLearning, r/learnmachinelearning.
• Join study groups, local meetups, university alumni networks, or Slack/Discord communities (Hugging Face, Papers with Code).
• Pair with peers in hackathons or open-source sprints.

10. Evaluate, improve, and iterate

• Prioritize improving the weakest parts: better evaluation, more realistic data splits, production constraints, or interpretability.
• Add ablation studies and clear comparisons to baselines.
• Make a small user-facing demo or video walkthrough — recruiters like demos.

11. Quick roadmap (first 3 months)

• Month 1: one vertical-slice end-to-end project (baseline model + demo).
• Month 2: a deeper technical project (research reproduction, deployment, or MLOps pipeline).
• Month 3: polish portfolio, write two blog posts, apply to internships/freelance or contribute to an open-source ML repo.

Final tips

• Prefer messy, realistic datasets over toy datasets when possible.
• Document tradeoffs and limitations: hiring managers value honest critique.
• Prioritize reproducibility and a working demo — these are disproportionately persuasive.
• Show impact (even simulated): explain how your model would be used in production and what metrics matter.

If you want, I can:

• suggest 3 project ideas tailored to your interests/skill level, or
• give a checklist/template README you can reuse for every project. Which would you like?