Machine Learning is a subfield of Artificial Intelligence that aims to provide machines with human-like capabilities. It is umbrella terms for a set of techniques that help computers learn to perform a task without being explicitly programmed. Machine Learning is capable of solving redundant tasks more efficiently than humans, which is why we are getting more and more dependent on technology.
The applications of machine learning touch every aspect of our lives since machine learning applications go far beyond the field of computer science. These applications are getting more and more sophisticated by the day due to the continuous generation of big data which helps power these machine learning models and make them more accurate.
Today, in 2021, we constantly interact with machine learning applications without even realizing it. Gone are those days when we used to rely on manual ways to run our errands. In this blog, we will be looking at some of the most commonly used applications of Machine Learning of the current time. These ideas can also be used in Machine Learning Hackathon
Machine Learning Application #1: Anomaly Detection
An anomaly is a data point that is different from the rest of the data points. Many times the anomaly is an unwanted data point, which does not help much in understanding the general trends in the data. On the contrary, since the number of non-fraudulent transactions and non-failures issues far exceeds the number of fraudulent transactions and failure issues, anomaly detection can be used to detect to automatically raise an alert when such a situation occurs.
Online fraud detection helps make online transactions safe and secure, making cyberspace secure. With the increase in the number of online transactions and payment channels, it is one of the most useful applications of machine learning in 2021.
Many banks use fraud detection before authorizing a transaction. Additionally, Paypal uses machine learning models to predict money laundering by comparing all the transactions and distinguishing between the legitimate and illegitimate ones. Anomaly detection also helps marks emails as fraudulent or legitimate or filter out malicious emails.
Since most of the tasks in recent times are automated, an unreported failure may lead to a huge amount of loss depending on how soon the failure is detected. Machine learning applications use anomaly detection to detect a sudden rise in the number of failed requests and also provide the necessary information about the failure and its causes, helping the business to quickly correct the source of failure.
Machine Learning Application #2: Prediction
Machine Learning applications are adept at understanding the trends in the historical or current data and making future predictions or the likeliness of an event to occur, based on this data, without any human intervention. Generally, random forest and artificial neural networks are the most common algorithms used to make predictions.
Some of the common applications which use predictions are:
1. Weather Prediction: Weather predictions help detect future incoming storms which can help the authorities to take the necessary safety measures. Predicting the rain patterns also helps farmers plan the harvesting times for their crops.
2. Traffic Prediction: We all probably heavily rely on Google Maps to assist ourselves with directions and traffic. It can detect real-time if you are on the fast route, the estimated time of arrival, or if there are any faster routes available. This helps us plan our journey better.
But how does it do that? Google Maps uses a combination of factors like the number of people currently using the service, historic data of the route, the average current speed of the route that the user plans to take, etc. to predict the upcoming traffic and adjust your route accordingly.
3. Stock Market Predictions: Many traders use algorithmic trading, based on machine learning applications, to predict the direction of the market and make trades where they buy low and sell high to make a profit on their transactions.
Understanding the Difference Between AI And Machine Learning on this link.
Machine Learning Application #3: Product Recommendations
Many big e-commerce platforms aim to make the most out of the user session by providing product recommendations similar to the ones you are currently browsing for or recommending other items that go well with the currently viewed item.
This is done to provide more targeted purchase options to you or increase the basket size. These recommendations are provided based on your purchase pattern and the purchase patterns of those users who are similar to you and have purchased a particular item.
Machine Learning Application #4: Healthcare Industry
Many healthcare-related companies using machine learning algorithms to make medical procedures more reliant. Using machine learning algorithms, the hospitals or clinics can predict the waiting times of the patients in the emergency rooms, based on the records of patients, staff availability, etc.
Doing so can help predict whether the hospital has the resources to treat the patient in a timely manner or some alternatives should be explored. Machine learning algorithms are also useful in scanning through the medical records of an individual for the early detection of disease so that it can be cured before it turns lethal.
Machine Learning Application #5: Image Recognition
The human mind has great cognitive skills, however, image recognition for a computer is a very difficult task. The primary reason which makes this problem tough is that, unlike humans who have a sense of depth, for machines, images are just some sort of numerical value. Image processing algorithms perform recognition by looking at the patterns in the digital images. This can be used to perform multiple tasks, such as:
- Facial Recognition: Many smartphones provide you the ability to unlock the smartphone by simply just looking at it.
- Biometrics: Many companies allow biometric-based entry-exit or attendance systems. Doing so is more preferred over facial recognition techniques before storing biometric information is comparatively easier and faster.
Machine Learning Application #6: Virtual Personal Assistants
As the name suggests, virtual personal assistants assist you in finding useful information on demand by voice or text. These VPAs use multiple machine learning models like speech recognition, speech-to-text conversion, natural language processing, text-to-speech conversion, etc. All you need to do is activate the VPA, ask the question and the model gets you the desired solution based on your data saved or accessible by the VPA.
Many companies are now utilizing VPAs to incorporate chatbots to provide a quicker solution to the customer’s most frequently asked queries and to reduce the number of queries that need to be manually resolved by the customer service team.
Machine Learning Application #7: Video Surveillance
Video surveillance systems have been in use for a long time to track people and detect unusual behavior. But imagine a single person monitoring multiple video cameras! It is boring and a difficult job, reducing the real-time effectiveness of surveillance tasks.
Machine learning applications help track individuals in the frame using object detection and object tracking techniques and give alerts of anomalous behaviors to human attendants, who can take over and help avoid mishaps.
Machine Learning Application #8: Sentiment Analysis
Human beings are emotional and their emotions determine their actions at a particular time. Hence, it becomes essential for good products to cater according to the current mood of the customer.
It uses a natural language algorithm to identify the words and then further uses artificial neural networks to identify the tone of the words (positive, negative, or neutral) and conclude the current emotion of the customer.
It is a real-time machine learning application that is used predominately to determine the user sentiments in the reviews, decision-making, etc. The following image shows a sentiment analysis done on the tweets as input and their respective outputs.
Machine Learning Application #9: Regression
Setting the right price for goods or services is an age-old problem in economic theory. There are a large variety of pricing strategies that can be utilised, but machine learning applications aim to predict the most effective price of a good or service based on the past data of how much other goods or services with similar attributes are worth. This also helps food delivery platforms or e-hailing taxi services to provide surge pricing when the demand exceeds the supply.
Machine Learning Application #10: Self-Driving Cars
One of the coolest and most sought applications of machine learning is that of self-driving cars. It’s here and people are already using it. The machine learning model trains on unsupervised data with the help of deep learning on crowdsourced data of vehicles, drivers, and their driving.
Use internal and external sensors as part of the internet of things technology and drive according to the data received from the sensors. Some of the common machine learning algorithms that drive self-driving cars are Scale Invariant Feature Transform (SIFT), AdaBoost, TextonBoost, You Only Look Once (YOLO), etc.
Frequently Asked Questions
The various tasks that can be solved by utilising the capabilities of machine learning algorithms are known as the applications of machine learning algorithms. With the current advancements in technology and generation of data, it is only a matter of time that the scope of these applications improves.
In 2021, machine learning is used to make almost all tasks efficient. Today, the applications of machine learning touch almost every aspect of our lives since the scope of the applications are not only limited to the computer science domain, but has improved to encompass other engineering domains as well.
You use machine learning everyday without even realising it. You open up your phone using fingerprint or facial recognition, use e-commerce platforms which provide you product recommendations, surf through your social media which brings you curated contents, etc. Even while you sleep, your smart watch tracks your sleep patterns and apply machine learning models on the data to predict the quality of your sleep.
Yes, Siri and Alexa use multiple machine learning algorithms like speech recognition, speech to text conversion, natural language processing, text to speech conversion, etc. to form a cohort of machine learning applications called VPAs (Virtual Personal Assistants).
DBSCAN Clustering In Machine Learning
The above 10 applications are what we believe makes machine learning one of the hottest skills to learn in 2021. Apart from the above applications, machine learning can be used to do a lot more tasks more efficiently than humans and it’s only a matter of time before machine learning starts empowering other fields of technology and engineering and disrupts even more industries.
By Saarthak Jain