Get to know ML .NET: Machine Learning Framework from Microsoft
The phenomenon of AI (artificial intelligence) is getting more and more popular these days. With the introduction of Chat GPT some time ago, AI has become a very popular technology. Of course, AI technology cannot be separated from the role of machine learning behind it.
Quoting from the Microsoft website, AI is a branch of computing that involves training computers to do things that normally require human intelligence. Machine learning is a subset of AI that involves computers learning from and finding patterns in data so they can then make predictions in new data themselves.
ML .NET is an open-source and cross-platform machine learning framework developed by Microsoft for .NET developers. This framework enables developers to easily build and integrate machine learning models into their .NET applications. In this article, we will discuss the introduction of ML .NET, what ML .NET can do, implementation examples, and reasons why to use ML. NET.
Introduction to ML.NET
ML.NET 1.0. Source: devblogs.microsoft.com
ML.NET was introduced by Microsoft for the first time in May 2018. Now, Microsoft has officially introduced two versions of ML .NET, namely: ML .NET version 1.0 in May 2019 and and ML .NET version 2.0 in November 2022.
ML.NET allows us to train, build, and ship custom machine learning models using C# or F# for a variety of machine learning scenarios. ML.NET includes features such as automated machine learning (AutoML) and tools such as the ML.NET CLI and ML.NET Model Builder, which make it easier to integrate machine learning into our applications.
What Can ML .NET Do?
Machine learning is a technology that has been growing rapidly in recent years. Many companies use machine learning to process and analyze data for useful information. With ML .NET, we can do many things, from sentiment analysis, and object detection, to price prediction. Here are some things that can be done with ML .NET:
- Sentiment analysis - Analyze sentiment from customer reviews using a binary classification algorithm.
- Product recommendations - Recommend products based on purchase history using a matrix factorization algorithm.
- Price prediction - Predict taxi fares based on parameters such as mileage using a regression algorithm.
- Customer segmentation - Identify customer groups with similar profiles using clustering algorithms.
- Object detection - Recognizes objects in images using the ONNX deep learning model.
- Fraud detection - Detects fraudulent credit card transactions using a binary classification algorithm.
- Sales spike detection - Detect spikes and changes in product sales using an anomaly detection model.
- Image classification - Classify images (for example, broccoli vs pizza) using TensorFlow deep learning models.
- Sales forecast - Estimate future product sales using a regression algorithm.