Application Programming Interfaces, or more commonly known as APIs, enable an application to relate, talk or share information with another application. As human beings it is very easy to misinterpret information or miscommunicate with each other but a computer will only share the exact information that is requested of it and in the format that it is has been programmed to.

In this article, we’ll attempt to define what machine learning is and how it’s altering business. We’ll also discover some popular web APIs that any developer can access to integrate image recognition, Natural Language Processing (NLP) as well as other predictive models into their software architectures.

What is Machine Learning?

Machine learning is the field of study that grants computers the ability to learn and act given initial programming constraints. It is everywhere these days and is so widespread that you might use it more than a couple times in a day without knowing. A simple example is your email; when you go through your inbox to mark items as spam, your email program then watches what messages you flag and automatically starts flagging those types of messages as soon as they come in again.

Machine learning, as a subfield of Artificial Intelligence (AI), when placed at the core of a digital business can play a huge role. Take for instance a web builder; AI could play a pivotal role in various aspects such as helping understand how subtle elements like page design can impact conversion rates. For real-world examples, take how Google has altered the advertising market by using machine learning to better target advertisements. Similarly, Amazon has disrupted the retail market with the addition of highly-aware product recommendations.

We can see how machine learning and the applications that use it are not only disrupting business but also making changes to our everyday lives. From virtual assistants to self-driving cars, machine learning and related APIs bringing extensibility are offering a fresh take on data and informing intelligence.

Machine Learning vs. Artificial Intelligence

Due to the recent efforts toward recreating the human thought process, the terms ‘machine learning’ and ‘artificial intelligence’ are often used synonymously. The principal structure of machine learning is that instead of having to be taught everything, machines can instead learn how to improve through observation; trying and failing just as a human would do.

As applications based on machine learning continue to evolve, so do the APIs that drive them. APIs extend data and functionality, enabling programs to interact with other applications. They’re largely invisible to most people, aside from developers who generally use them to pull information from other applications and synchronize data.

Source

Machine Learning APIs

The Machine learning APIs in use today offer a wide range of capabilities, such as image and facial recognition, speech recognition, text analysis, natural language processing, sentiment analysis, predictive modeling, pattern recognition, and document classification. These APIs are constantly refined by their creators on functionality, best practices for ease of use, documentation, and other criteria.

We have comprised a list below of some of the tried-and-tested machine learning API options on the market. Though they only represent a small fraction of the vast variety of products, the following APs have in one way or the other gained from machine learning and have been arranged according to their capability categories.

Image and Facial Recognition

IBM Watson Visual Recognition

The IBM Watson is one of the most popular machine learning platforms that works fluidly with cognitive computing. Its visual recognition program understands image contents and can virtually classify any visual content, find images that are similar in a collection, select human faces, display the estimated age and gender of a face and also be trained to classify your own visual content by adding custom image classifiers.

Launched in November of 2013, its Developer Cloud suite offers a collection of APIs that not only make use of Visual Recognition but also give developers the ability to create applications that make use of Natural Language Processing (NLP), prediction, and other machine learning technologies.

Google Cloud Vision API

This API can detect the emotions that are associated with faces and can also recognize words that are printed in different languages. It is capable of creating rich annotations, scaling them, and classifying images into different categories. It can also predict the content of an image and help find what image you are looking for based on its classification, such as; phone, cat, or even boat.

Kairos API

Kairos is a human analytics platform that allows you to combine the emotional analysis of a person, their demographic data, their facial identity, and other metadata into any of your services or Apps.

Text Analysis, NLP, Sentiment Analysis

Google Cloud Natural Language API

This API is used to provide natural language processing and to analyze the meaning and the structure of text for developers to understand. Examples of this include; Text annotations, sentiment analysis and entity recognition.

Microsoft Azure Text Analytics API

Built with the Azure machine learning technology, this is a suite of text analyzing services that can be used to evaluate unstructured texts for purposes such as phrase extraction, language and topic detection, and sentiment analysis.

IBM Watson Language

This API is used to teach a computer how to perform text analysis by reading. It can convert unstructured data into structured ones as is used in business intelligence, targeting advertising, and social media monitoring.

Predictive Modeling and Other Machine Learning

Amazon Machine Learning

The application of this API covers areas such as; Targeted marketing, forecasting demands, click prediction, and fraud detection as this API is mostly used to find patterns of occurrence in data. The algorithms present in it use these models to treat new data and create predictions for an application.

Google Cloud Speech API

This converts audio to text in a variation of over 80 languages by using accurate and quick speech recognition collected from a file or through a microphone.

Business Benefits

There are many business implications of implementing machine learning. Brendan Wilde, Marketing Manager at Free Parking says “Machine learning is everywhere, from recommending products to improving your shopping experience on Amazon, to assisting the voice-controlled interface in your connected car to understand you.” With a machine learning API integrated into your business, it serves as a method to inform your data and fine-tune end user experiences.

When handled properly, machine learning APIs are capable of letting enterprises reach farther than their traditional offerings to find new customers, markets, and services using their data. They are also able to create new strategies and add value to already existing offerings, like the ability to market to customers through influencers as opposed to the customers themselves.

Machine Learning Use Cases

The potential use cases for businesses adopting these types of APIs are numerous, but some examples include:

Recruitment: Most businesses tend to get more than a bucket load of job applications, which could be very time consuming when trying to process. If HR provides a job description and a preferred CV type, a pattern could be determined, and a machine learning API could sort through data to find suitable candidates more easily.

Counterterrorism: There has been a general urgency in the intelligence community to be able to predict and prevent attacks from terrorists. With machine learning, the required urgency could be achieved, and models could be created to add insights, test hypotheses, and deploy them effectively.

Marketing: Incorrect targeted marketing through email and even direct mail is expensive and can make prospects feel like they’re getting spammed, thereby hurting a brand. Machine learning could be used to increase your targeted accuracy by determining which of the prospects need the material.

Conclusion

With the overall progress that the Internet and technology have made over the years, there isn’t a better time than now to implement machine learning in various applications. A prediction from IDC Futurescapes has stated that digital transformation is the next corporate strategy and two-thirds of Global 2000 Enterprise CEOs are willing to focus on it. And with this transformation, machine learning solutions will spearhead how these enterprises can view and work on their customer value.

Data, however, is key to introducing machine learning into any application for business. We are in an information era and it is very necessary for a business to collect data from an array of sources like partners, suppliers, and customers. It is recommended to thus start with machine learning solutions that have already been incorporated into standard business software rather than creating a totally new use case which requires a high level of expertise — that’s where APIs come in. This way, you can immediately begin to create new value for your business.