10 real-time applications where Machine learning is the best fit
We are going to discuss real-time applications where machine learning is the best fit. If you try to solve it using a traditional algorithm it will be almost impossible to solve it.
- Analyzing images of products on a production line to automatically classify them → This is performed generally using (CNN) conventional neural networks.
- Detecting tumors in brain scans → This is semantic segmentation, where each pixel in the image is classified (as we want to determine the exact location and shape of tumors), typically using CNNs as well.
- Classifying News Articles → This is natural language processing (NLP), and more specifically text classification, which can be tackled using recurrent neural networks (RNNs)
- Flagging Offensive comments → Done using NLP
- Summarizing long documents automatically → Done using NLP
- Creating a chatbot or a personal assistant → This involves many NLP components, including natural language understanding (NLU) and question-answering modules.
- Forecasting your company’s revenue next year, based on many performance metrics
- Making your app react to voice commands → This is speech recognition, which requires processing audio samples: since they are long and complex sequences, they are typically processed using RNNs, CNNs, or Transformers
- Detecting credit card fraud
- Recommending a product that a client may be interested in, based on past purchases → This is a recommender system. One approach is to feed past purchases (and other information about the client) to an artificial neural network and get it to output the most likely next purchase. This neural net would typically be trained on past sequences of purchases across all clients.
Credit: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow