The one word that we have frequently been hearing over the years is Artificial intelligence (AI). It has been changing the business landscape and heading the technology to its higher levels and is showing its impact on almost all industries across the globe. It is creating a new tech world where minimal or no human intervention is required to complete a task.
Artificial intelligence consists of different subfields, which are machine learning, Neural Networks, Computer Vision, Robotics, Speech processing, Natural language processing, Evolutionary Computation, Planning, and Expert systems. In this article, we are going to discuss Machine Learning Applications With Examples in today's world. Before this let's have a small introduction about what is artificial intelligence.
What is Artificial Intelligence?
Artificial intelligence is defined as the ability of a computer or its program to act like a human being in certain situations such as solving a problem, learning, speech recognition, planning, etc. Today the Artificial Intelligence is widely used in almost all industries to create a smart production environment at low cost and with minimised risk.
What is Machine Learning?
Machine Learning is an application of AI, and it enables the systems to self-learn from the experiences without being programmed explicitly. Machine learning helps the systems in developing the algorithms and to take relevant data as a resource for self-learning. Its capabilities have exceeded human expectations and placed it in many industries.
Till now, we have come to know the abilities of AI and now, let us discuss the industries which are using ML in their regular conduct of business to reach their desired levels.
Below mentioned are Top Machine Learning applications across various Industries. Let’s discuss them in detail.
Real-World Applications Of Machine Learning(ML Models) - Latest
#1) ML Applications in Retail
Machine Learning has become the latest trend in the retail industry, and retailers have started implementing big data technologies such as Hadoop and Spark to eliminate the problems involved in data processing. And processing these data sets and keeping them idle would yield nothing out of it. So, to make use of this data, the retailers started implementing the machine learning algorithms. These algorithms will use the data sets to automate the analysis process and help the retailers in achieving their desired growth.
Let's consider an example
Have you ever come across similar products or products that match your taste while using an online shopping portal? Most probably you may have. All these things happen due to the application of machine learning algorithms. Many retailer giants across the globe are using different machine learning algorithms to induce the customer to buy their products and services. For example, Alibaba is using “E-Commerce Brain”, and AWS is using Neural Network algorithms to send product recommendations to the customers.
#2) ML Applications in Travel
Machine learning has paved a new way in the modern world of transportation. Self-driving cars are capable of ha/ndling the driving by letting the driver relax or have leisure time. Let's consider Uber transport here. Uber uses different machine learning algorithms to make a ride more comfortable and convenient for the customer. Did the below-mentioned questions ever pop up in your brain?
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How does uber decide a fair for a ride?
How does Uber enable ridesharing by matching the destinations of different people?
How does it minimizes the waiting time once you are done with the booking of a ride?
To get the answers to all these questions, we must understand the below concept.
Uber has invented a pricing model called Surge pricing by using the machine learning model, and later it was nicknamed as “Geosurge” at Uber. This model can recognize the traffic patterns to charge appropriate fair to a customer. In 2011, Uber charged customers almost double price due to heavy traffic and an increase in demand.
Uber leverages predictive modelling in real-time by taking into consideration traffic patterns to estimate the supply and demand. Using surge-pricing model, the drivers will come to know the areas where demand is going to be high and they have a chance to prepare in advance to reach there, hence the surge pricing would decrease to a great extent.
#3) ML Applications in Finance
Huge frauds are happening every year and billions of dollars are being stolen by hackers across the world. It shows an adverse impact on the country’s economy and on the people who have lost their hard-earned money. To solve this kind of issues, Machine learning has come up with its innovative solutions.
Have you heard the incidents like someone receiving the calls from the bank and knowing that they had a suspicious transaction in their account? It is just because of the machine learning algorithms. Machine Learning is widely used in banking and finance for fraud detection purpose.
Machine learning can scan the high volume of data sets. It even examines vast amounts of financial transactions and identifies the occurrence of any unusual activity.
Every time a consumer makes a transaction, the ML algorithm will give fraud detection score, and if any time the score goes beyond the normal levels, it automatically blocks the transactions. So the unauthorized user will no more have accessibility to a particular account. Without using a machine learning application, it is challenging for a human to detect fraud from thousands of transactions.
Let's consider some examples here.
- City bank collaborated with fraud detection company known
- as Feedzai that works in real-time to identify the fraudulent activities and report back to the customer or bank to react on them immediately.
- Paypal (an international online financial service company) is using Machine Learning algorithms to control the money laundering. It uses several machine learning tools which can compare the billions of transactions with one another and determines what is accurate and what is not, among the buyers and sellers.
#4) ML Models in Healthcare
The medical industry will soon be going to experience the new wave. The doctors and medical practitioners are going to expect accurately how long a patient will be still alive. Medical systems are becoming smarter and started learning from the data to suggest the required tests by eliminating the unnecessary ones. Machine learning algorithms will replace the Radiologist job.
The massive amounts of healthcare data is being generated and stored for analysis, but the analysis is impossible to do by human beings. Machine learning finds a way to see patterns and evaluates the unstructured data automatically. ML allows doctors to design a particular medication known as Precision Medicine.
Machine learning is revolutionizing healthcare with its self-learning methods. It eliminates unnecessary tests and minimizes the cost and human intervention. Its advancements in healthcare such as precision medicine, robotic surgery, and creating smart electronic records will result in the improved life span of the patients and decreases costs.
#5) ML Models in Social media
Social media has created a revolution in the tech world. Back in the initial days, we used to engage in social media only to chat with friends and to see the activities of the people around the world. If we want to communicate any business information, we had to communicate via emails or visiting the company website to get the desired information.
Nowadays the scene has changed. 95 per cent of Millennial brands have stated that they can promote their brands via social media giants like Facebook, Twitter, Instagram, etc. 42 per cent of brand marketers have reported that Facebook is playing a crucial role in their day to day business.
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Facebook is being loaded with 1.5 million pieces of user-generated data each day, and machine learning helps the organizations in analyzing this vast amount of data sets. A recent study shows brands who react to feedback have got more customers loyalty than those who don’t. Machine learning algorithms or Bots have learned to respond to the questions of customers instantly by using data and natural language processing. Hence, it resulted in eliminating the tedious task of human beings, and they can use the meantime to engage in other major works.
We are living in an information era, where brands have access to a vast customer base, but the problem comes with the customer segmentation. It is nothing but targeting people with the content they are actually interested in, based on their past activity and demographics.
Brands have started implementing the big data and machine learning technologies to target the specific segment. Now, organizations can convey the message that they understand and pushing the ads, deals, and offers that appeal to them across different channels.
#6) ML Applications in Mobile apps
The mobile usage has been increasing day by day, meanwhile, the usage of mobile applications has also increased. Every application has to maintain some unique features to deliver the personalised content that is being liked by its users.
Mobile applications have started implementing machine learning to tailor the application according to the needs of every single user. Adding machine learning to develop the mobile application would result in acquiring the new customer base by maintaining older one strong and stable.
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Below mentioned are the advantages of mobile apps with machine learning.
- Customized feeds and content that meets the users’ requirements.
- It makes searches faster and easier.
- It Improves sales and revenue.
- It helps in driving more customers to your site based on the search made by general people, purchase patterns, site content, etc.
- Fast and protected authentication process.
Machine Learning is an incredible subfield of AI, and its contribution to the development of technology is invaluable. We have seen countless breakthroughs from the implementation of Machine learning in different fields. It has made many processes simpler wherever it was involved. It’s been continually surging towards the development of each field with its implications.