10 Machine Learning APIs You Should Learn

Machine learning is everywhere these days, from the photos on your phone to the filtering system in your email Inbox. Machine learning has become one of the most key components of the future. 

With the trend of the internet becoming more personalized, machine learning has become more important now than ever. Even big companies like Amazon use machine learning algorithms to provide you with recommendations based on your interests.

Around a decade ago, the main purpose of the internet was to provide you with information — one keyword would generate results from around the globe on that particular keyword. 

But today, the focus is to provide users with more relevant information — something that is closer to what they are searching for. This is where machine learning plays a big part. At this moment, machine learning is dominated by big companies including Google, Amazon, IBM, Microsoft, but the trend is now shifting and smaller companies are bringing their algorithms and APIs into the field. APIs are making it easier for companies to share knowledge and information across multiple spectrums. 

 Before we delve into a few innovative machine learning APIs, let’s take a look at what an API actually is.

 

What Is an API?                                                                    

An API, or an Application Programming Interface, is, in the simplest terms, a code snippet that allows two software programs to communicate with each other. It is a set of definitions, protocols, and tools for building software. An API is the link between two software, and it is responsible for sending requests from one software to another, as well as returning the request.

An API is made up of two parts — a specification that describes how information is exchanged between programs and as a software interface written to that specification and published in some way for use.

There are three types of APIs:

1.     Local APIs — These APIs offer OS or middleware services to application programs, such as Microsoft’s .NET APIs.

2.     Web APIs — These APIs work across the internet to send and receive information. These include URLs.

3.     Program APIs — These are based on Remote Procedure Call technology that a remote program component appear to be local to the rest of the software.

10 Trending Machine Learning APIs That We Think You Should Learn in 2019:

1. PredictionIO

PredictionIO is an open-source machine learning API that is built on Apache that makes it easier for data scientists to build predictive machines. It can be easily bundled with Apache Spark, MLlib, HBase, Elasticsearch, and Spray. It uses a unique template system for creating machine learning systems that make it easier for developers to customize the engine according to their own needs.

PredictionIO can also automatically evaluate a prediction engine to determine the best hyperparameters to use. This amazing API takes on the major task, allowing developers to simply add their own customization to the mix. PredictionIO offers features such as quick build and deployment of an engine, customizable templates, respond to dynamic queries in real-time, faster machine learning modeling with systematic processes, pre-built evaluation measures, simple data infrastructure management, etc.

2. Geneea Natural Language Processing API

Geneea is a natural language processing API that can perform analyses on raw information provided. This API can perform analyses on information such as raw text, on the text extracted from the given URL, or directly from the provided document. 

Developers can also provide additional information, such as language used, particular domain, etc. that can help make the results more precise. Geneea performs analyses on topics such as language, correction, diacritization, tagging, topic detection, name entity recognition, etc.

3. IBM Watson Visual Recognition

IBM Watson’s Visual Recognition API uses machine learning algorithms to correctly identify, classify, and tag objects. It can also be used to search for visual content such as color, find human faces, tag an image, approximate age and gender, and even find similar images in a collection. 

Developers can even create and train custom classifiers to identify objects that they need. The IBM Visual Recognition is part of the larger IBM Watson Developer Cloud suite of APIs that also includes speech to text, text to speech, question and answer, personality insights, tone analyzer, etc.

4. Slack API

Slack became one of the most popular workplace communication tools a few years back, and since then, it has introduced its own API to allow developers to build their own customized communication system for their workspace. This RESTful API allows developers to learn and use the Slack codes. 

It offers Slack’s powerful natural language processing functionality, which allows developers to build applications that integrate with Slack, such as intelligent chatbots or other bots that can schedule meetings.

5. AT&T Speech

AT&T Speech API allows developers to integrate speech-recognition capabilities to their applications. The API is powered by the AT&T Watson speech engine and also includes Natural Language Processing features such as natural language understanding, speech recognition, speech transcription, and many more. It can easily transcribe a spoken word file to text. The API can be tuned to fit specific needs such as Web Search, Business Search, Voicemail, SMS, Question and Answer, etc.

6. Microsoft Cognitive Service — Text Analysis

Microsoft has been making strides when it comes to machine learning. This popular API allows developers to automatically detect that language of the text before translating it.

 It can also extract information from your text including language and the sentiment behind the statement. It also offers other features such as key phrase extraction, language detection, sentiment analysis, translation, and even identify entities in your text.

7. Amazon Machine Learning

Amazon’s machine learning API can perform a lot of different functions. It has the capability to perform functions such as fraud detection, content personalization, document classification, and customer churn prediction. 

It also allows developers to quickly train and deploy their models. However, Amazon’s API is not open-source, it is available for a pay-as-you-go payment plan.

8. BigML

BigML is a Machine learning REST API that allows developers to easily build and deploy AI models for your apps. This API allows building predictive models that include supervised and unsupervised machine learning tasks, as well as machine learning pipelines. The best part is that BigML allows for creating, retrieving, updating, and deleting BigML resources using standard HTTP methods.

9. Google Cloud APIs

Google has always been into innovation, and the one place where it really shines is machine learning. Google has an entire suite of Cloud APIs that have been designed to help simplify a developer’s tasks. Google’s machine learning APIs include Cloud Vision API, Cloud Speech API, Natural Language API, Translation API, and Dialogflow API.

• Cloud Vision API — includes image labeling, detection for face, logo and landmarks, optical character recognition, and detection of explicit content.

•  Cloud Speech API — includes speech recognition, audio conversion from a microphone or a file, conversion to text in over 80 languages.

•  Natural Language API — includes structure analysis, meaning of text, sentiment analysis, entity recognition, and text annotations. 

• Translation API — Translates from one language to another. 

• Dialogflow API — A complete development suite for conversational interfaces such as chatbots, voice-powered apps, etc.

 

10. Wit.ai

Wit.ai is a natural open-source language processing platform that offers the function to add intelligent speech functionality to web and mobile applications. It offers an intelligent voice interface for applications such as home automation, connected cars, smart TV, robotics, smartphones, wearables, etc. 

The documentation for Wit.ai is clean and easy to understand. It includes code samples, SDKs for many popular languages and platforms, quick start guides, and a complete Wit app guide.

Conclusion

With machine learning here to stay, developers will really have to up their game if they want to remain in the competition. These 10 APIs should help you get an edge over the others.

 Written by: Shweta Datar