Software Development10 Cool APIs you should know in Machine Learning

10 Cool APIs you should know in Machine Learning

Machine learning has been leaving its impact on every advent of technology. Be it in the photos of one’s phone or acting as an important part of one’s email inbox’s filtering system, machine learning is gaining importance in the quotidian life of an internet user. Machine learning plays a vital role in focusing to provide all internet users with relevant information that suits all their keywords for that particular search. Big companies are trying to bring their machine learning algorithms and APIs into one field. Now, you must be thinking what is API? Here is a guide to all your doubts about this technological term:


API or Application Programming Interface is the code that allows two software programs to communicate with one another. The set of definitions, protocols, and tools for building up software are all laid down by these APIs. It also helps to send and receive requests from one software to another. Now let’s discuss the trending machine learning APIs that one should know to be at par with the latest technology needs:




    When this machine learning is integrated with API, it helps developers to build applications based on the models set by Amazon Machine Learning to find a particular pattern or patterns in the data. The fields where this API is used are in fraud detection, demand forecasting methods, targets in marketing and click prediction. This also provides visualization tools and wizards that help to create machine learning models without getting involved with the complex part of ML algorithms and technology. In fact, Amazon Sage Maker is being offered to simplify machine language for novice developers to focus on the data science of building, training, and tuning of machine learning models.


    This cloud-hosted machine learning and data analysis help users to set up a data source, create a dataset, build a model from the dataset and then make predictions accordingly. It also helps to integrate the functionality of this application with others and create new ones too. It has expertly taken out the complexities of Machine Learning to focus on enhancing and automatic decision making. It also provides robustly engineered algorithms combined with Machine Learning to solve the real world problems with the help of a simple and standardized framework for the company.


    Being a web-based machine learning program, this helps to automate the recognition tasks that were performed manually earlier. This platform can be used for modeling and visualization of data, team collaboration, and computing of GPU. All these functions can be performed from within a browser. It also helps developers to make HTTP requests to access the platform in a programmatic way for creating, retrieving, and updating of objects related to datasets, models, predictions and ensembles. It allows a maximum file of 10mB per API call to get uploaded using the API.

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    Founded by Mani Doraisamy and Boobesh Ramalingam, this machine learning platform simplifies the task of integrating the machine language with applications. Its main focus is to predict the intents of customer and it does so by using the Google Prediction API in its engine layer to improve the accuracy of its prediction. It also uses social media data for building up of customer personas and combines it with the business data of a user to bring forward the interests and actions of customers.

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    In order to integrate the world’s most powerful AI into one’s application, one can use the integration of Watson on the IBM cloud. This will also help to store, train and manage one’s data in the most secure cloud. The services that this API provides are:

  1. Unlocking of the hidden data to get answers, monitor trends and surface patterns too.
  2. Building and deploying chatbots and virtual agents across a wide range of channels.
  3. Tagging and classifying of visual content using machine language.

The Watson is believed to have human-like capacities of image recognition, language processing and reasoning of services. Some good examples of this API are ‘medical diagnostic apps’.


    This API is believed to build the next generation Image Recognition Applications that also offer customized machine learning technology. It is an all in one Image Recognition Solution for many developers and businesses that come with the following functions:

  1. The powerful API helps to assign tags to one’s images for a better image analysis and discovery.
  2. Instant image classification is possible with this API that also leads to automatically categorizing the image content.
  3. Helps in custom training by organizing the photos in one’s own list of categories.
  4. The powerful API helps colors to extract meaning from the product’s photos.
  5. Content-aware cropping by generating automatic and beautiful thumbnails is made possible by this API.
  6. Leads to adult image content moderation based on the state of the art image recognition technology.
  • NuPIC API: 

    This open source project is written in languages like Python or C++ and implements Numenta’s Cortical Learning Algorithm (CLA). The properties of this API are:

  1. Sparse distributed representations
  2. Temporal inference
  3. Online learning

It also allows developers to work with raw algorithms, bring together multiple regions along with their hierarchies and make use of other platform functions.


    This API mines, fuses and structures data from a varied number of sources for the creation of long-range predictions clubbed with data-driven insights. Their Hyper Contextual Data Processing takes into account details of weather conditions, geography, traffic, and prices to examine the conditions for vacations and activities for specific places and time too. Its algorithms ingest big data and compare all destinations for carrying out this function.


    This Face API from Sightcorp helps to build and infuse face functionality by using AI-based apps that make customer interaction more engaging. Its face API has the following features:

  1. Face detection
  2. Analysis of emotions
  3. Demographics of a crowd
  4. Analysis of human attention
  5. Cross-platform
  6. Analysis of crowds

They also help to integrate apps with a fast, accurate and anonymous face analysis with the following facts:

  1. Expressions exactly reflected on the face
  2. Estimation of age
  3. A close estimation of gender
  4. Ethnic origin
  5. Estimation and evaluation of mood
  6. Estimation of one’s head pose
  7. Style of clothing of a person

    This API helps to build intelligent algorithms into apps and websites. It also empowers data scientists to develop and manage skillfully the AI solutions with the help of CLI and Python tools and a wide range of Azure data too. It is also an efficient collaborative drag and drop tool to build, test and deploy analytic solutions on one’s data.

These are the top 10 APIs that one should know for working with Machine Learning. It is necessary to know about these APIs as these are a generic connectivity interface offered to an application. Their modern applications are very developer friendly, easy to access and comprehensible too. These comprehensive APIs are constantly paving the way for user-friendly, innovative and highly customizable experience.




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