Top 20 APIs You Should Know In AI and Machine Learning

Well, in a nutshell, there are two main options: text evaluation (sentiment analysis, engagement, emotion) and photograph evaluation (facial emotion, facial localization).

The great advantage is this API is free to use, and no training data is required, so you can try it right now.

APIs for Face Detection and Face Recognition9.

Animetrics Face RecognitionDocumentation: http://api.


com/documentationDemo: http://api.


com/demoIf you want to create a face recognition software or simply conduct image analysis, Animetrics Face Recognition will support you a lot on this matter.

First of all, you can use it for detecting faces in photos and pictures and matching them against a set of known faces.

Another advantage here is information on facial features, or landmarks are returned as coordinates on the image.

What is more, this API can also upload or put off a subject from a searchable gallery, and upload or eliminate a face from a topic.


Eyedea RecognitionDocumentation: http://face.


cz:8080/api/face/docsDemo: http://cloud.


cz/api/faceEyedea Recognition is a real giant in the world of object detection and objects recognition.

This API perfectly handles a bunch of software solutions prepared according to customer specification and based on cutting edge research results in machine learning and artificial intelligence.

This flexible recognition service offers eye, face, automobile, copyright, and plate detection.

The most significant value of the API is an opportunity to get right of entry to instantaneous information of objects, customers, and behaviors.


BetafaceDocumentation: https://www.



php/documentationDemo: https://www.



htmlAll you need to know about this API is that it is a robust and scalable platform for scanning uploaded files or photo URLs, detect faces and examine them.

This API includes such capabilities, as a couple of faces detection, faces cropping, 123 face points detection (22 basic, one hundred and one advanced), faces verification, as well as similarity hunt in very massive databases.


ImaggaDocumentation: https://docs.


com/Demo: https://imagga.

com/auto-tagging-demoOne more powerful API for image analysis, instant image classification, color extraction, and content-aware cropping.

Imagga gives APIs that automatically assign tags for your shots and makes your pictures findable.

It is based on an image recognition Platform-as-a-Service.

Text Analysis and Natural Language Processing APIs13.


aiDocumentation: https://wit.

ai/docsDemo: https://labs.



htmlIt is an extensible NLP-platform.

If you want to empower developers routine related to voice automation, it will be the best option for.

Personally I am a big fan of this API.

The reason it appeals to me remains their focus on comprehending human language from every interaction and leverages the community, which means everything that was learned will be shared across developers.

Wit creates an intelligent voice interface for purposes like home automation, connected cars, robotics, smartphones, wearables, etc.

Plus, it is free to use.


BitextDocumentation: https://docs.



com/Demo: http://parser.


com/Bitext API is another deep linguistic analysis tool providing data that’s easy to export to a whole spectrum of data management tools.

The platform product can be used for chatbots and assistants, CS and Sentiment, as well as some other core NLP tasks.

The main focuses here are on semantics, grammars, lexicons, and corpora available for more than 80 languages.

Plus, this API is one of the best when it comes to customer feedback analysis automation.

The company claims to deliver insights with 90% accuracy.


GeneeaDocumentation: https://api.


com/Demo: https://demo.


com/Geneea performs analysis (Natural Language Processing) on the supplied raw text, on the text extracted from the given URL, or directly from the provided document.

The great advantage here is a considerable number of available languages (more than 30).

Geneea performs analyses on topics such as language, topic identification, sentiment detection, entity extraction, automatic tagging, as well as various correction like diacritics for Czech text.


Diffbot AnalyzeDocumentation: https://www.


com/dev/docs/Demo: https://www.


com/This API carries out automatic identification, analysis, and extraction that typically delivers every piece of data (text, photos, video) from any URL with ease.

It properly employs an ideal mixture of AI, ML, computer vision and NLP.

Plus, you can equally use it with custom APIs that enable data to be taken using manual regulations.


Yactraq Speech2TopicsDemo: https://yactraq.

com/contact-trial/It is great speech analytics with a purpose to free up the capability of your audible data.

This API converts audiovisual content material into subject matter metadata thru speech recognition & NLP.

It gives a set of call operations solutions that offer excessive ROI, massive statistics applications, and may shine a comprehensive light on surprisingly precious data.


MonkeyLearnDocumentation: https://monkeylearn.

com/api/v3/Demo: https://monkeylearn.

com/contact/MonkeyLearn is an AI platform that allows you to classify and extract actionable data from raw texts like emails, chats, webpages, documents, tweets and more.

It is especially focused on minimizing the time needed for all these tasks and so it is one more good pick for this list!19.

Hu:tomaDocumentation: https://help.


ai/article/ym34wr87lx-hutoma-chat-apiIt is an open-source conversational AI-powered platform that helps to simplify access to actionable data via natural language interfaces and AI-assistants.

If you are implementing a Natural Language Interface into your app or website this is going to be your number 1 priority.

The reason for this is you can teach and train it by feeding it examples of conversations (movie scripts, support logs.



nlpToolsDocumentation: http://php-nlp-tools.

com/documentation/The last but not the least one, nlpTools is an open source simple text processing framework (a library for NLP written in php) to analyze Natural Language.

It decodes online news media (general-purpose, multiple topics) for sentiment analysis and textual classification.

The Final WordDuring reading, you have probably noticed a few options that you want to apply.

But before going any further and making the final choice, I want to draw your attention to one important thing.

Using only such a plan for ready-made solutions, especially for beginners, is not good at all.

Why so?.Without going into more complex processes now, you risk not cope with the real task in the future.

Hence, you need to obtain a healthy balance between all of this, but I’m sure you already know that.


Thank you for reading!.I hope it will help you to level up your game.

If you know some other good picks, please suggest them in the comments below.

I will share my posts on my blog on topics around AI, ML, Data Science and e-Learning.

You can also follow me on this journey through my social media platform — Instagram.

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