Machine learning is the hottest topic in the market, with a lot of attention in various fields. Sadly, machine learning algorithms have been frightening for people who are not so knowing a lot about modern technology or data science specialists. 

Thanks to the Machine Learning APIs that perform it easier for people to study and use machine learning methodologies. A Machine Learning API operates just like any regular API by generating an abstraction layer for developers to combine machine learning into the day-to-day apps that they create. Let’s review the most common machine learning APIs used now.

Machine Learning APIs for Data Science:

IBM Watson Discovery API

A great cognitive exploration and content analytics engine that enables developers to recognize patterns and trends and other actionable insights. Such output from the API can be applied to drive better decision-making. 

The main elements of Watson Discovery API combine IBM Watson, IBM Watson Natural Language Processing, IBM Watson Assistant, Personality Insights, IBM Watson Visual Recognition, IBM Watson Speech to Text, etc.

Popular use cases:

1. Translating text to many other languages.
2. Managing the reputation of a phrase or word with a planned audience.
3. Making forecasts of social aspects of a person from the provided text.

Google Cloud APIs

Google Cloud API runs on REST as well as RPC. Google Cloud APIs elements like Speech API , Vision API, and Natural Language API are most asked after for current world demands. Vision API request includes reading published and handwritten text, recognizing faces and objects etc. 

Developers can change audio to text by applying Google’s Cloud Speech API that going on powerful neural system models. Natural Language API is an excellent pre-trained pattern that helps developers work with natural language knowing like sentiment analysis, syntax analysis, entity analysis, etc.

Popular use cases:

1. Ford manages Google’s Cloud API for records, the driver to build a list of ways and places that the driver regularly visits. This assists in predicting better navigation ways for the driver.
2. Fraud discovery can be easily made with Google APIs, and many businesses give it off as a service to outside customers.

BigML

BigML is a really user-friendly RESTful API covered around machine learning algorithms. Users can make and run imminent models efficiently. The BigML API can be applied for performing basic managed and unsupervised machine learning functions as well as building machine learning pipelines that have incredibly high levels of complexities. 

Unlike many other established APIs, BigML gives the users total access to datasets, models, clusters, and anomaly indicators. Other specialties include providing a near real-time forecast, command-line interface, and web interface.

Recommended use cases:

1. Performing what-if situation analysis conditions for business analysts by building a clear pattern for relationships among the various characteristics and properties in multiple data
2. Building applications that need cyclic predictions. The old data can be put on BigML platform and then can be re-used next.

Amazon Machine Learning API

Amazon Machine Learning API is made on the Amazon cloud staging. It explains the algorithms for making forecasts that require lots of professional expertise on developing the model, restoring the data and performing analytical analysis. 

The API also gives data visualizations based on the forecasts. Other highlights of the Amazon Machine Learning API involve creating UI support levels, algorithmic limitations, wizard-driven GUI. All these innovations, along with Amazon’s guarantee of integrity and user-friendliness, has made Amazon Machine Learning API, the top selection of developers. 

Common use cases:

1. Analyzing the genre of the song by explaining the sound signal levels and backgrounds.
2. Person Activity Recognition by examining the sensor data obtained from a gyroscope, smartphone or smartwatch. The API can determine whether the person is sleeping, standing or sitting, walking upper or downstairs.
3. Traffic prediction by analysing user movements during the first week or first-month.
4. Identifying bots, fake users and spammers by monitoring website movement records.

The Amazon Incentives API supports the requisition and delivery of the Amazon Gift Codes. Receivers can instantly redeem and use their bonuses to buy on the Amazon. There is a lot of similar API like Amazon Incentives API, for example, another gift card platform.

Developers can also use the different functions and automation innovations of the API to streamline inventory control and enhance operational performance. The API’s documentation is available upon signing up for an account through a link given at its portal homepage. The signup is a simple registration method that enables developers to access regulatory and technical assistance from the company.

Geneea Natural Language Processing API

Geneea Natural Language Processing API supports users to leverage the text data for normal language processing (NLP). It essentially offers four types of unrestricted APIs – General API (G3), VoC API, Media API and Intent Detection API. General API is a general-purpose API that does sentiment analysis, language discovery and other linguistic analyses.

The Media API supports media attention to detect news reports are about, allowing unique tags to editorials etc. The Voice of the Buyer API (VoC ) helps users to analyze client feedback, identifying the issues that clients are talking about, etc. the intent detector API serves to detect the purpose of a text.

Kairos API

Kairos API is the easiest of all with a unique main feature of face recognition. Users can include face recognition in their software outputs very efficiently, applying the API. Its notable features include separation of gender detection, diversity recognition, age groups, searching for human faces in photos and videos, searching for matching faces, etc.

Prediction IO

PredictionIO is wholly made on top of the open-source machine learning server doing open-source development techniques and languages. The salient characteristics include simplifying data infrastructure administration, unifying data from various platforms, simplifying database management, comprehensive predictive analytics etc. It also helps other data processing and machine learning libraries and OpenNLP and Spark MLLib.

Microsoft Azure Cognitive Service

This is essentially a Text Analytics API giving powerful natural language processing pieces over raw text. It is cloud-based, giving a bunch of examples of AI and machine learning algorithms. The main points include key phrase extraction, language discovery, sentiment analysis and named entity recognition. 

These innovations are already being used in their goods like Bing and Xbox. But they are being delivered to customers only the recent past.

TensorFlow API

TensorFlow API is the easiest and most efficient way of building and performing TensorFlow graphs. The API is more language flavored and is possible in languages like Go, Python, JavaScript, C++, and Swift. These developers favor using Python as it is more simple to use. 

Machine learning is a large and complex science and people have created libraries and API to get the developer’s life more comfortable.