Table of Contents

10 Applications Of Machine Learning

Table of Contents

Machine Learning is powered by insights, the pace and quality of algorithms improvements rely not as much on computing power or impeccable code. Rather, on high quality data, if the company knows how to collect information from its users, monitor on-site interactions and listen to social media posts, they can achieve remarkable results, as shown by Google, Amazon, Netflix, Microsoft, Facebook.

Let’s take a look at machine learning applications in multiple industries to see how this technology can be applied to real-life problems!

#1 Image Recognition

The neural network takes a ready library of images and analyzes them pixel by pixel to detect objects and features on images. Each neuron offers insight after validating their piece of contents and the network unites millions of these conclusions into a cohesive analysis.

The great example is Clearview – a facial recognition technology that analyzes data from social media to get insights on people’s faces and get their data.

#2 Social Media Analysis

Machine learning can analyze millions of posts on Facebook, Twitter, Instagram, read comments and personal updates. Machine learning allows systems not only to recognize words but understand the context behind them.

Just imagine, what if a machine learning system analyzed your Facebook feed, which conclusions will the system make?

It can help businesses keep up with customer feedback, track their brand health and improve reputation.
The LionBridge project is a great example. This sentiment analysis tool provides users with insights, based on social media posts in more than 300 languages.

#3 Smart Assistants

With machine learning, Smart Assistants can analyze personal data, process voice requests, automate daily tasks and adapt to changing user needs.

Alexa by Amazon for instance, uses all collected data to improve its pattern recognition skills and be able to address new needs.

#4 News Classification

As the amount of produced content grows exponentially, businesses and individual users need tools that would classify and sort out the information. The algorithms can run through millions of articles in many languages and select the ones that are relevant to user interests and habits.

#5 Video Surveillance

Machine learning can help develop complex algorithms for video recognition. At first, using human supervision, the system will learn to spot human figures, unknown cars and other suspicious objects. Soon, it’ll be possible to imagine a video surveillance system that functions entirely without human supervision.

#6 Optimization of search engine results

Machine learning algorithms can use user habits and interests from analyzing search statistics. The rating algorithms won’t rely on meta tags and keywords, but instead, will analyze the context of the page. Google RankBrain is a great example of this idea.

#7 Email Analysis

Machine learning techniques can analyze and compare legitimate emails with spam and determine differences even in cases where humans would easily make a mistake.

#8 Speech Recognition

Machine learning helps the software to adapt to dynamic speech patterns. Users use idioms, slang, abbreviations and to stay flexible, a system needs to learn all these altered versions. This is where machine learning is essential. Even theoretically, a human team can’t teach millions of speech variations to the software manually. If the system trains itself however, the task becomes much more manageable.

#9 Cyber Security

Machine learning algorithms can immediately detect cyber security threats. The system will recognize the threat, analyze similar cases and take measures to secure the website or application. It allows businesses to be up to date with malicious practices and predict safety issues before they even come up.

#10 Customer Service

Machine learning algorithms analyze customer behavior. From this data, chatbot developers can know which issues to focus on. As soon as several dozens of responses were confirmed, the chatbots can learn on their own from daily interactions with clients, getting better with each dialog.

Machine learning is a long-term investment that delivers continuous improvements as your business grows. The software equipped with ML algorithms, will become better with every next client interaction, analyzed database or text file. The more it works, the more precise its insights become. Which is a perfect success formula for scalable businesses?

This blog was prepared by the Eztek team. We provide software engineering, UI/UX design, testing and IT consulting services. Don’t hesitate to reach out!

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