Machine Learning Examples In Real World

  • (4.0)
  • | 1311 Ratings

Machine Learning Examples In Real World

Machine learning is a technology termed by Arthur Samuel in 1959 who took Computer games to next level with artificial intelligence while his stint with IBM. As per Samuel, these computers have the ability to learn without the need of structured programming & supervision for every action. Ever since then machine learning has become a separate stream within computer sciences. There are other technologies like data mining or predictive forms of data analysis which is completely different from machine learning.

Are you intereted in taking up for Machine Learning Course? Enroll for Free Demo on Machine Learning Training

Machine Learning Examples

Google Assistant

This is a virtual assistant delivering its services by recognizing user’s voice. This application replaces mouse clicks and touches on the screen. Anyone can verbally command for the services required on the computer or internet which is delivered. Google has the most updated version of Voice recognition Machine Learning Engines. Prior to it, Apple designed Siri, Amazon has Alexa & Microsoft has Cortana. Anyone having access to all four products can clearly find that Google Assistant is the smartest. It is based on Google natural language processing algorithm. Nowadays this assistants can engage itself in a two-way conversation by interacting with visual representations on screen.


Facebook has designed its own social networking algorithm which identifies persons from contact list with a remarkable technology and requests the current user to invite them to join Facebook. Facebook also pulls relevant content from its server to its viewer. The content that is being delivered is not based on the number of views. This is why organizations are now paying more attention on the content and increasing the relevance score on their Facebook page. This increases the engagement chances of the advertiser’s products rather than having a view of the page only. There are many other social networking applications like twitter which is facing an extreme challenge from Facebook as compared to the engagement of advertiser’s products. Brands are sold more with these innovative AIs with more relevant content than having followers or viewers only.

Google Maps

The feature that we currently see describing the traffic of nearby area is an excellent feature using Machine Learning algorithms. It finds out through its vast network of Smartphones on each and every road and determines the time taken by the Smartphone to cover a distance and quickly informs nearby users about the possible delays. This algorithm is based on Dutch Mathematician Dijkstra’s algorithm based on shortest route theory. The algorithm is considered to be one of the simplest but most elegant. ETA, Expected time of Arrival is also calculated on the same process.

Google Search

Based on Knowledge graph Algorithm, which decodes the content of the query by utilizing the suggestion of the previous viewer. It also deploys PageRank which is a link analysis system. PageRank was designed by Larry Page who is the founder of Google Inc. The algorithm counts the number and qualitative links to other websites and makes a rough estimate of the website’s importance. Few links to important websites will have a higher page rank as compared to websites having many external links. This algorithm has more chances of manipulation and is currently being studied to check its misuse.

Gmail’s Smart Reply

As we all know the more precise we can speak and write, the more efficient professional we can become. To exhibit our professionalism, Gmail has introduced a smart feature with three different options of replying to mails by automatically generating subject and content of the mail. As we go through mails, we can quickly acknowledge anyone, even when we are in a meeting, giving respect and honor to the mailer. It is based on Smart Reply Algorithm which triggers the computer & designs three best responses to a mail as soon as it hits the inbox.


As we all know banks are the biggest playfield for fraudsters, which needs continuous monitoring. PayPal designed an artificial intelligence engine built in-house. It uses open source tools, which created a bond between human fraud analysts and its artificial intelligence (AI) engine. Armed with its own AI, PayPal is way ahead in business as compared to any other banks in the world. It transacts $11000 worth of payments every second for approximately 2 Billion customers in 200 countries. The AI used by PayPal also differentiates between false alarms which restricts panic to its customer from true fraudulent activities. Hui Wang, senior director of Global Risk at PayPal reiterated their desire to stop intelligent fraudsters at the door.


As we move ahead in social networking, similarly giant strides have been made in streaming of High definition videos. But streaming needs a huge amount of data to be transferred, whereas most of the countries have very slow internet connections. To cope up with the situation, Netflix uses an Artificial based algorithm which analyses frame by frame and compresses it accordingly for faster transfer of data. It compresses to the magnitude which is required without degrading the pixels in the frame.  The machine learning system is termed as Dynamic Optimizer which fixes the bandwidth issue in emerging economies like India, South Korea and Japan.

Frequently Asked Machine Learning Interview Questions


Machine learning section of Uber has hired top scientists in this field to advance the lives of global citizens by conducting researches to issues like self-driven cars & urbanized aviation including safety of passengers. Recently Uber has launched an AI that charges its customer based as per their will and not on a fixed rate in terms of distance travelled. Customers of varied demography can be differentiated on socio economic factors and are charged accordingly which can make the lives of other members of the society avail the facility. Uber has termed this as route based pricing system. Prices soar if the route isn’t common & a premium is charged.


It’s an UK based fashion retail portal which used Machine Learning Algorithms ever since its startup to become retailer without any boundaries. We have seen many retailers have their space bindings to stock their products catering to customers within a territory. Artificial Intelligence engines has helped brands and products based even in a remote area to make its presence felt in every part of the globe. AI has in fact helped fashion designer to concentrate more on designing niche products and allowing AI to look after the logistics and creating new markets. This system also provides deep insights into the consumer behaviour which helps the fashion designers to keep updated too.


This machine Learning Engine is really learning deep to create ultimate music playlists as per the preferences of the user. Spotify’s music recommendation has become so intelligent that it continuously updates with the users data interaction in background and is nowadays recommending on weekly new releases too. Spotify is also helping artists from new genre to promote themselves by reaching out to audiences who are actually looking for similar performances. This helps the artist to receive more likes than dislikes which keeps the performer motivated. The machine learning engines are going stronger and stronger day by day taking the customization level to skyrocketing heights.


To conclude, these examples are only some drops extracted from a sea of usages and other utilities. Many corporates are using this Machine Learning Algorithms as a tool not only to reduce cost but also to attract new tech-savvy customers. Brands targeted youngsters are increasingly resorting to AI to pull customers. They have understood that it’s better late than never.

Subscribe For Free Demo

Free Demo for Corporate & Online Trainings. Protection Status