Great — here’s a curated list of hands-on machine learning projects you can build to learn progressively, from beginner to advanced. Each entry includes the goal, key skills, suggested datasets/tools, milestone steps, and an estimated time-to-complete. Pick projects that challenge you but are still achievable — iterate and document as you go.

Beginner (good for first 1–3 projects)

1. Predict house prices (regression)

• Goal: Predict sale price from tabular features.
• Skills: Data cleaning, feature engineering, linear regression, tree models, evaluation (RMSE, MAE).
• Datasets/Tools: Kaggle “House Prices — Advanced Regression Techniques” or any local housing dataset; Python, pandas, scikit-learn, matplotlib/Seaborn.
• Milestones: EDA → handle missing values → baseline linear model → tree-based model (RandomForest/GradientBoosting) → cross-validation → simple feature engineering → short report/notebook.
• Time: 6–15 hours.

2. Classify handwritten digits (image classification)

• Goal: Build classifier for digits (0–9).
• Skills: Image preprocessing, train/validation split, simple neural network or classic ML (SVM/KNN), evaluation (accuracy, confusion matrix).
• Datasets/Tools: MNIST or Fashion-MNIST; TensorFlow/Keras or scikit-learn.
• Milestones: Load data → preprocess/normalize → baseline model (logistic regression) → small CNN → evaluate and visualize errors.
• Time: 6–12 hours.

3. Titanic survival prediction (binary classification)

• Goal: Predict survival from passenger info.
• Skills: Feature engineering (categorical handling), basic models, evaluation (ROC, precision/recall).
• Datasets/Tools: Kaggle Titanic dataset; pandas, scikit-learn.
• Milestones: EDA → create features (title, family size) → baseline model → tuning → short write-up.
• Time: 4–10 hours.

Intermediate (build on core ML + begin real-world issues) 4) Sentiment analysis on product reviews (NLP)

• Goal: Predict sentiment (positive/negative) from text.
• Skills: Tokenization, embeddings/TF-IDF, text classification (logistic regression, LSTM, Transformer fine-tune), evaluation (F1).
• Datasets/Tools: IMDb, Amazon reviews, Yelp; scikit-learn, spaCy, Hugging Face Transformers.
• Milestones: Clean text → baseline with TF-IDF + logistic regression → try pretrained embeddings or fine-tune a small Transformer → error analysis.
• Time: 10–25 hours.

5. Time series forecasting (retail or energy)

• Goal: Forecast future sales or consumption.
• Skills: Time series decomposition, feature creation (lags, rolling stats), ARIMA/Prophet/GBM/LSTM, backtesting.
• Datasets/Tools: Retail sales datasets (e.g., Rossmann, Kaggle), Facebook Prophet, statsmodels, scikit-learn, TensorFlow.
• Milestones: Visualize series → stationarity checks → naive baseline → engineered features + tree model → advanced: seq2seq or Prophet → backtest and quantify uncertainty.
• Time: 15–30 hours.

6. Customer segmentation (unsupervised learning)

• Goal: Segment customers for marketing using clustering.
• Skills: Feature scaling, dimensionality reduction (PCA, UMAP), clustering (KMeans, DBSCAN), business interpretation.
• Datasets/Tools: E-commerce transactional dataset or UCI datasets; sklearn, seaborn.
• Milestones: Aggregate RFM features → scale → try PCA/UMAP → cluster with KMeans → profile clusters and present actionable insights.
• Time: 8–20 hours.

7. Build a recommender (collaborative + content-based)

• Goal: Recommend items to users.
• Skills: Matrix factorization (SVD), neighborhood methods, content features, evaluation (precision@k, recall@k).
• Datasets/Tools: MovieLens; Surprise library, implicit, scikit-learn.
• Milestones: Baseline popularity model → collaborative filtering (SVD) → add content-based features → evaluate with holdout.
• Time: 15–30 hours.

Applied / System-focused projects 8) Deploy an ML model as an API

• Goal: Serve a trained model behind a REST endpoint.
• Skills: Model serialization, Flask/FastAPI, containerization with Docker, simple logging/metrics.
• Tools: Python, FastAPI, Docker, any trained model (e.g., image classifier).
• Milestones: Save model → build API endpoint → containerize → local test → add simple health-check and sample client.
• Time: 6–15 hours.

9. Build an interactive data app (Streamlit / Gradio)

• Goal: Make a UI to explore a model or dataset.
• Skills: UI design, model inference, user input handling, basic UX.
• Tools: Streamlit or Gradio, hosted on Streamlit Cloud or Heroku.
• Milestones: Prototype UI → integrate model predictions → add visualizations and explanations → deploy.
• Time: 4–12 hours.

Advanced (for deeper learning or portfolio pieces) 10) Object detection in images (computer vision)

• Goal: Detect and localize objects with bounding boxes.
• Skills: Data annotation/augmentation, transfer learning (Faster R-CNN, YOLO), loss functions, evaluation (mAP).
• Datasets/Tools: COCO subset, Pascal VOC, detectron2 or YOLO frameworks.
• Milestones: Prepare dataset → fine-tune pretrained detector → evaluate and optimize → speed/accuracy tradeoffs.
• Time: 40+ hours.

11. Build a production-ready ML pipeline (MLOps intro)

• Goal: End-to-end pipeline: data ingestion → training → validation → deployment → monitoring.
• Skills: ETL, scheduling (Airflow), model versioning (MLflow/DVC), CI/CD, monitoring.
• Tools: Docker, Kubernetes (optional), MLflow, Airflow, Prometheus/Grafana (monitoring).
• Milestones: Simple pipeline that pulls data, trains and registers model, deploys inference endpoint, and collects prediction logs for drift detection.
• Time: 40–100 hours.

12. Sequence-to-sequence model (machine translation or summarization)

• Goal: Build or fine-tune a seq2seq model for summarization or translation.
• Skills: Transformer architectures, tokenization, evaluation metrics (BLEU, ROUGE), fine-tuning and decoding strategies.
• Datasets/Tools: WMT small sets, CNN/Daily Mail for summarization, Hugging Face Transformers.
• Milestones: Baseline rule-based approach → fine-tune pretrained transformer → tune decoding (beam search, length penalty) → evaluation and qualitative review.
• Time: 30–80 hours.

13. Anomaly detection in logs/metrics

• Goal: Detect unusual behavior in time-series or system logs.
• Skills: Feature extraction, unsupervised learning (Isolation Forest, autoencoders), streaming considerations.
• Datasets/Tools: Synthetic logs or public datasets; scikit-learn, PyOD, TensorFlow.
• Milestones: Define anomalies → feature pipeline → baseline unsupervised detector → thresholding and evaluation with labeled anomalies (if available).
• Time: 15–40 hours.

Project ideas for speed & variety

• Build a chatbot for a narrow domain using retrieval + RAG (retrieval-augmented generation).
• OCR pipeline to extract structured text from scanned documents.
• Image style transfer or GANs for basic image synthesis.
• End-to-end fraud detection model for credit-card transactions (class imbalance handling).

How to get the most learning value

• Start small: get a working baseline quickly, then iterate.
• Track experiments: log hyperparameters and metrics (W&B, MLflow, or just a spreadsheet).
• Focus on errors: perform careful error analysis and visualizations — that’s where the learning happens.
• Reproducibility: containerize or provide a requirements.txt and seed random states.
• Write a short project README or blog post explaining dataset, approach, results, limitations, and next steps — great for a portfolio.
• Learn tooling: Git, Docker, and some deployment experience are valuable in interviews and the workplace.

If you want, I can:

• Suggest a 4–8 week learning plan using 3–5 of these projects tailored to your experience and goals.
• Give a step-by-step notebook outline for any single project above (with code snippets and exact libraries).

Which would you like next?