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The ML.Net for Delphi (mldotNet4Delphi) are sets of APIs that can help you train, build, and ship custom machine-learning models. It gives you ability to add machine learning to Delphi applications. ML.Net for Delphi (mldotNet4Delphi) is a machine learning framework for Delphi developers that enables integration of custom machine learning models into Delphi applications. Machine learning applications make use of patterns in the data to make predictions rather than needing to be explicitly programmed. Central to ML.Net for Delphi is a machine learning model. The model specifies the steps needed to transform your input data into a prediction. With ML.Net for Delphi, you can train a custom model by specifying an algorithm. Once you have a model, you can add it to your application to make the predictions. The ML.Net for Delphi runs on Windows currently and in future it will be available in Linux and macOS. What ML.Net for Delphi can do: - Sentiment analysis: Analyze the sentiment of customer reviews using a binary classification algorithm. - Product Recommendation: Recommend products based on purchase history using a matrix factorization algorithm. - Price Prediction: Predict taxi fares based on parameters such as distance traveled using a regression algorithm. - Customer Segmentation: Identify groups of customers with similar profiles using a clustering algorithm. - Fraud Detection: Detect fraudulent credit card transactions using a binary classification algorithm. - Sales Spike Detection: Detect spikes and changes in product sales using an anomaly detection model. - Sales Forecasting: Forecast future sales for products using a regression algorithm. - Image classification: Classify images (for example, broccoli vs. pizza) using a TensorFlow deep learning model.