Computer Vision:

Develop an image classification model to automatically classify images into categories like people, animals, landscapes, etc. Train a CNN model on a large dataset like ImageNet.
Build an object detection model to identify and locate objects within images. Train a model like YOLO or SSD on a dataset of your choice.
Create an image segmentation model to segment images into pixel-level categories. Train a model like UNet on a medical or satellite imagery dataset.
Develop an automated visual inspection system using computer vision and deep learning to detect defects in manufactured products.

Natural Language Processing:

Build a text classification model to classify documents or sentences into categories. Train on a tagged dataset like IMDB reviews or Amazon product reviews.
Create a text summarization model to automatically summarize long-form text like news articles or documents. Train an abstractive summarization model on a large dataset.
Develop a machine translation system to translate text between two languages using an encoder-decoder model. Train on a parallel text corpus.
Build a named entity recognition model to extract entities like people names, locations, organizations from free-form text. Train a model on a tagged NER dataset.

Time Series Forecasting:

Build forecasting models using LSTM networks or Prophet to predict and analyze time series data like stock prices, sales numbers, weather patterns etc. Train on a long history of time series data.
Create an energy usage prediction system using past smart meter data to forecast household or city-level energy consumption. Train recurrent models on meter reading datasets.
Develop forecasting models to predict customer churn, credit risk, disease outbreak based on historical time-series profiles of customers, loan applicants or populations.

Recommender Systems:

Build a movie/product recommendation engine using collaborative filtering on a database of user preferences/transactions. Develop and evaluate different CF algorithms.
Create a music recommendation system using both content-based and collaborative filtering approaches. Integrate genres, attributes, lyrics, user play histories.
Develop an article/content recommendation tool for a news/magazine site making use of user profiles, article topics/embeddings and user-article interactions.

Deep Reinforcement Learning:

Train an agent using DRL techniques like DQN or PPO to master games like Atari, Go or Chess using raw pixels/states as input. Analyze training curves, hyperparameters.
Develop an intelligent traffic signal control system using DRL to optimize traffic flow in a simulated city environment.
Create an robotic arm controller using DRL to perform pick-and-place tasks in a simulated warehouse setting. Optimize for speed, efficiency.

Healthcare:

Build models for medical image analysis – classify skin lesions, detect diseases in X-rays/CT scans. Evaluate on public datasets.
Develop risk prediction models for diseases using clinical notes, lab tests and other health metrics as features. Ensure privacy and ethics.
Create predictive models for ICU triage, ventilator allocation, surgical pathology using time-series EMR data from hospitals.

Fraud/Anomaly Detection:

Build credit card fraud detection system flagging anomalous transactions based on spending patterns, location, device etc. Evaluate on private labeled transaction datasets while maintaining privacy.
Develop a log anomaly detection solution to flag security threats, malware, DDOS attacks by learning “normal” patterns in server/network logs.

Some key aspects to focus on in a capstone project are – selecting a meaningful problem and dataset, applying suitable machine learning techniques, training high performing models, thorough experimentation, rigorous evaluation, reporting results with visualizations and insights. The project demonstrates research skills, technical abilities and communication skills. Proper documentation of code, experiments and findings is also important for a high quality capstone.

Overall machine learning capstone projects offer opportunities to apply academic learning to real-world applications across industries while gaining hands-on experience in end-to-end machine learning pipelines. The above examples illustrate a range of possibilities within different domains. Selecting a well-scoped, impactful project aligned with your interests and expertise enables a fruitful capstone experience.