This content originally appeared on DEV Community and was authored by Seth Bang
Image recognition: Build an image recognition system that can identify objects in images. You could use the popular ImageNet dataset and a pre-trained neural network like VGG or ResNet, or train your own model.
Sentiment analysis: Create a sentiment analysis system that can classify text as positive, negative or neutral. You could use a dataset of movie reviews or tweets to train your model.
Chatbot: Build a chatbot using natural language processing and machine learning. You could use a framework like TensorFlow or PyTorch to create a neural network that can understand and respond to user queries.
Recommendation engine: Create a recommendation engine that can suggest products or content to users based on their browsing or purchase history. You could use a collaborative filtering approach or a content-based approach to build your model.
Fraud detection: Build a fraud detection system that can detect anomalies in financial transactions. You could use a variety of machine learning algorithms, such as logistic regression or random forests, to train your model.
Spam filtering: Create a spam filtering system that can classify emails as spam or not spam. You could use a dataset of emails and apply techniques like Naive Bayes or SVM to train your model.
Time series forecasting: Build a time series forecasting system that can predict future values based on historical data. You could use a variety of techniques, such as ARIMA, LSTM or Prophet, to build your model.
Object detection: Create an object detection system that can identify and locate objects in images or videos. You could use a pre-trained model like YOLO or Mask R-CNN, or train your own model.
Style transfer: Build a style transfer system that can apply the style of one image to another image. You could use a pre-trained model like Neural Style Transfer, or train your own model.
Voice recognition: Create a voice recognition system that can identify speakers and transcribe their speech. You could use a pre-trained model like DeepSpeech, or train your own model.
These are just a few ideas to get you started. The possibilities are endless when it comes to machine learning projects, so feel free to get creative and come up with your own ideas.
This content originally appeared on DEV Community and was authored by Seth Bang
Seth Bang | Sciencx (2023-04-05T20:41:04+00:00) 10 Impressive Machine Learning Projects to Add to Your Python Portfolio. Retrieved from https://www.scien.cx/2023/04/05/10-impressive-machine-learning-projects-to-add-to-your-python-portfolio/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.