What are some unique ML portfolio projects to impress potential employers?

A less nefarious project here would be to a multi-document summarizer which is something I've implemented before. Essentially you crawl several major newspapers, use unsupervised clustering or near duplicate detection to find multiple stories about the same event (such as an iPhone launch or missile strike). Then for each group of related stories, you can then implement a summarizer to create a whole new article combining multiple sources using a LLM or extractive summarization. This works particularly well for creating well-rounded news coverage that considers various biases and perspectives in a single article.

The task involves creating fake news articles using machine learning techniques, particularly natural language processing (NLP). This project is both challenging and controversial. It requires skills in text generation, sentiment analysis, and topic modeling to produce convincing fake news. You can use existing datasets or gather data from the internet. Additionally, you can compare various models like GPT-3, BERT, or LSTM to determine which one generates the most realistic fake news.

Predicting stock prices is a common and useful application of machine learning. This project involves analyzing historical data and market trends to forecast stock prices. It requires expertise in time series analysis, regression, and deep learning. You can utilize diverse data sources like stock prices, news, social media sentiment, or economic indicators to develop and train your predictive model. Additionally, you can explore various visualization and interpretation techniques, such as candlestick charts, moving averages, or trading signals, to enhance your predictions.

Detecting deepfakes, which are deceptive media created using ML to fabricate realistic but misleading content, is crucial for safeguarding security, privacy, and democracy. This project involves applying computer vision and image processing techniques like face detection, face recognition, and face swapping to identify and uncover deepfakes. You can utilize established datasets like FaceForensics++ or employ tools like DeepFaceLab or FaceSwap to create your own dataset for this purpose.

Movie recommendation is a prevalent and valuable ML application that relies on user preferences and behavior. This project involves demonstrating expertise in data analysis, data mining, and recommender systems. You can utilize datasets like MovieLens or Netflix to create and assess your movie recommendation system. Additionally, you can implement various recommendation approaches, including collaborative filtering, content-based filtering, or hybrid systems, to determine which one delivers the most pertinent and personalized movie suggestions.

Add your own twists to some of the projects that you see suggested out there! If you're reading these articles and just replicating the stated ideas as is, then it won't be as valuable, since the idea isn't really yours. And if it isn't really yours, you can be sure that many other people will have the same project on their portfolio. This is a reason why I think the value of guided capstone projects (common in online courses or bootcamps) are in the skills that you learn, but not as a showcase project in your portfolio, because it doesn't distinguish you as a candidate. It is ok to use commonly available datasets, but always try to ask your own questions of data - make the research question in your analysis yours.