“Machine Learning” is defined as “computer’s ability to learn without being explicitly programmed”. This article describes a few of the machine learning applications in real life.
What is machine learning?
Machine learning is a branch of artificial intelligence. It focuses on the development of algorithms that can learn and make predictions on data. It aims at overcoming programming limitations and make data-driven decisions. Machine learning was developed from pattern recognition. It was believed that computers can learn without being programmed to do so. This can help the machine adapt and improve over time.
Applications of Machine Learning
Virtual Personal Assistants
Siri, Google Now and Alexa are some of the virtual assistants that we use every day. These have been trained to answer to voice commands using machine learning. Virtual assistants are also included in smart speakers such as Amazon Echo, Google Home, etc. Other examples are Samsung Bixby on Samsung S8, mobile apps like Google Allo and many more.
It is difficult to determine traffic conditions at any given point. This is because all cars are not equipped with GPS services. Instead, traffic conditions are predicted on a combination of available GPS data and daily traffic experiences. Uber decides cab ride prices on a machine learning algorithm which uses a variety of factors like rider demand at any particular time, available riders in the area, etc.
Machine learning algorithms are used in video surveillance to detect unusual behavior like standing still, stumbling, napping on benches, etc. The system can then alert human authorities who can further take appropriate actions.
Social Media Services
Facebook, Instagram, Pinterest and other social media platforms use machine learning to show targeted ads. Facebook uses machine learning to identify people in images uploaded. Pinterest uses machine learning to display similar pins.
Email Spam Filtering
Email services provide many spam filtering techniques. These need to be regularly updated to detect spam mail. Multilayer perceptron, C 4.5 Decision Tree are some of the spam filtering techniques.
Online Customer Support
Most websites provide online customer support. However, not all of them have a person answering all the questions. A lot of the websites use chatbots to answer user queries. The chatbots are trained by machine learning to understand user queries and give answers from the information provided on the websites. They learn over time with different query inputs. Later, the chatbots can give more specific answers to questions.
Search Engine Result Refining
Search engines use machine learning to improve search results. They observe user behavior when they are going through search engine results. If a user clicks multiple websites on the first page, then it assumes that it provided accurate results. On the other hand, if the user travels to the second or third page without going through available results, it decides that the results were not relevant.
When you visit an online shop like Amazon, Flipkart, etc. and add something to cart, the site shows you similar products or other relevant products. Machine learning helps here.
Online fraud detection
Online payment systems are trained to identify fraudulent transactions. The machine learning algorithms study the usual payment pattern of any customer and then identify any unusual activity. It helps prevent money laundering. PayPal uses machine learning to identify legitimate and illegitimate transactions.
Machine learning has a number of applications in healthcare. Some of them are as follows:
– It can help predict how long a patient with a terminal illness can live.
– Machine learning can help in the initial screening of drug compounds to identify their effectiveness.
– It can also predict the success rate based on biological factors.
– Doctors can understand disease progression and effects. They can design personalized treatment for patients.
– It can reduce the cost of drug manufacturing.
Cyber Security is high in demand. The number of ransomware and malware attacks is rising. There is a need for software that can identify these attacks easily. Most ransomware is based on its previous architecture with slight modifications. These changes can be difficult to identify for a person. However, machine learning algorithms can help train the software to identify the minute changes and prevent attacks.
Above mentioned are just some of the machines learning applications in our everyday lives. There are many such applications available. Machine learning helps create better AI applications.