There are more than 50 APIs suitable for machine learning and prediction applications. What is your choice? (2018)-Alibaba Cloud Developer Community

What a wonderful thing it is to have ready-made modules or tools for people doing engineering projects and scientific research, you can complete the corresponding task without accessing the source code or understanding the details of the internal working mechanism. The common method is to call some APIs, namely some predefined functions, to provide applications and developers with the ability to access a set of routines based on a certain software or hardware. This article summarizes more than 50 APIs that are useful to machine learning industry, mainly in the following areas:

  • Face and Image Recognition (Face and Image Recognition)
  • Text Analysis, natural language processing, emotion Analysis (Text Analysis, NLP, Sentiment Analysis)
  • Language Translation (Language Translation)
  • Machine Learning and prediction (Machine Learning and prediction)

this paper lists some mainstream APIs in each research field, and their order is alphabetical.

Face and image recognition

  • 1.Animetrics Face Recognition: This API can be used to detect faces in images and match them with known Face datasets. In addition, you can add or remove a category from the searchable gallery, or add or remove a facial image from a category.
  • 2.Betaface: This API provides face recognition and WEB service detection. Features include multi-face detection, face cropping, 123 face feature point detection, face verification and recognition, and similarity search in large databases.
  • 4.Face imagery: provides facial recognition and detection services. You can call this API to train programs, such as Face detection, Face recognition, group Face recognition, Face creation, and information retrieval.
  • 5.FaceMark: This API can detect 68 feature points on the front and 35 feature points on the facial contour.
  • 6.FaceRect: This API is very powerful and free. It can detect faces or multiple faces in front and outline photos, and output the detection results in JSON format. In addition, the API can display the detected eyes, noses, facial features such as mouth.
  • 7.Google Cloud Vision API: published on TensorFlow platform, enabling the model to learn and predict the content of the image. In addition, it can also help users search for their favorite images and obtain their annotations quickly and accurately. It can also be divided into thousands of categories according to ships, Lions, Eiffel Tower, etc., to detect facial related emotions and identify printed languages in multiple languages.
  • 8.IBM Watson Visual Recognition: This API can understand the content of images, such as image markers, detect face, age and gender predictions, and also detect face similarity. You can also use this API to develop services based on your own tasks.
  • 9.Imagga: This API can automatically assign labels to images so that images can be found. It is based on image recognition platform services.
  • 10.Kairos: This platform allows developers to quickly add emotion analysis and face recognition to applications and services.
  • 11.Microsoft Cognitive Service - Computer Vision: This API can analyze visual content in different ways based on input or user selection. For example, Mark images based on content, classify images, detect faces and return coordinates, identify content in specific areas, generate content descriptions, identify text in images, and Mark adult content.
  • 12.Skybiometry Face Detection and Recognition: This API provides Face Detection and Recognition services. The latest version of this API can distinguish dark glasses from transparent glasses.

Text analysis, natural language processing, and emotional analysis

Language Translation

Machine learning and prediction

Author information

Pedro Lopez, personal homepage of data scientists: this article is translated by Alibaba Cloud Yunqi community. The original title of the article is 50 + Useful Machine Learning & Prediction APIs, 2018 Edition, Translator: Begonia, Revision: Uncle_LLD. The article is a simple translation. For more details, please see the original text.

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