Top 5 Applications of Machine Learning

Machine Learning is a very interesting technology and a subcategory of Artificial Intelligence which is everywhere around us today. With the help of machine learning, computer programs learn, adapt and improve themselves on the basis of past data and experiences. It also allows systems to identify patterns, perform tasks and make predictions, accordingly. This technology requires very less human interference as machines autonomously access data. As more data is fed into machines, the algorithms teach the systems, which ultimately presents and delivers better results. For example, if you request Alexa to play your favorite song, she will go through the list of your most played songs and then pick one to play. You have to give her commands if you want to make changes such as adjust volume or skip a song. ML applications do not require explicit programming. As they are fed with new data, they learn, change, and develop on their own. Through an iterative process computers are able to find insightful information independently.

Applications of Machine Learning

All the industries immensely benefit from machine learning technology, especially those that have massive amounts of data to handle. Many of them have realized its significance and value and are taking maximum advantage to stay ahead of their competitors. Let's closely see the various sectors that are making the most out of this technology.

1. Retail Sector

ML is being implemented by many shopping and retail companies who prioritize their customers. From capturing and analyzing data to running marketing campaigns, gaining customer insights, and price optimization, it is present everywhere. Based on the purchase history of customers, retail websites suggest products using machine learning technology. They also use conversational Chatbots to engage with them. To satisfy their customers and provide them a personalized shopping experience, sellers adopt various ML techniques. Netflix and YouTube are the perfect examples of companies that heavily depend on ML. They recommend movies, shows and videos to their viewers or users based on their past searches and viewing history.

2. Healthcare sector

A number of devices and wearables have sensors inside them for example fitness trackers, sleep trackers or smart health watches. Such devices are used to check, assess and monitor the overall health and fitness of patients or users in real time. ML algorithms are very useful for medical practitioners and experts to make accurate predictions about the lifespan of a patient dealing with a deadly disease. Making or discovering a new drug is very costly and a long process, but through ML it is easy to analyze large volumes of data.

3. Travel sector

All the ride sharing apps such as Uber, Careem and InDrive incorporate machine learning, especially for the dynamic pricing. Companies also use it to analyze the reviews of users and also for the brand, compliance or campaign monitoring. The demand and supply as well as the traffic patterns are taken into account. Also, real time predictive modeling is used for the fare and ride booking. 

4. Finance sector

Banks and financial institutions make use of machine learning technology in order to analyze large volumes of data. Cyber security teams can also get information and warnings regarding fraudulent activities so that they can deal with them in a timely manner. ML also provides valuable insights to the investors related to investment opportunities.

5. Social Media

Machine learning has played a crucial role in showing user specific ads through the personalization of news feed. A plethora of users can connect and engage with each other through different social media platforms and networks. Through ML and image recognition, Facebook recognizes your friend’s face so that you can automatically tag them on posts.

Machine learning has influenced all industries around the globe and will continue to do so, since computer algorithms are becoming more efficient with the passage of time. Overall, an upward trajectory is expected for machine learning in the coming years.