We live in an era of self-driving cars and online streaming apps with powerful recommendation engines. Machine learning solutions have numerous applications in our day-to-day lives.
Whether it is individuals that use Google Maps to predict traffic or enterprises to build software that derives data-based insights, Machine Learning has penetrated almost every aspect of our digital lives.
Healthcare, Finance, Aviation, eCommerce, Education, Logistics – every industry has been transformed by adopting and implementing Machine Learning-based models. These models are trained using Python, a programming language considered as the most ideal for developing Machine learning models.
Machine Learning programs developed using Python are easy to integrate into other software, offering businesses a significant functional and cross-team collaborative advantage.
Known as a branch of Artificial Intelligence (AI), Machine Learning uses data sets and algorithms to replicate how humans learn, slowly enhancing its accuracy over time. There are three ways through which it does this.
1. Supervised Learning
Supervised Machine Learning solutions use labeled historical input and output datasets to train algorithms that are able to classify data and predict outcomes. This enables the Machine Learning algorithms to generate outputs that are as close as possible to the desired result.
2. Unsupervised Learning
Unsupervised Learning allows the Machine Learning algorithm to look for and identify patterns and then use data gathered from those patterns to make decisions. This Machine Learning model is useful when creating predictive models.
3. Reinforcement-based Learning
Reinforcement-based learning is perhaps the closest Machine Learning method and is almost similar to how humans learn. In this kind of Machine Learning, the algorithm interacts with the environment to get a positive or negative reward.
In simpler terms, the learning agent is able to perceive its environment, interpret it, and learn from it through trial and error.
Technological advances in storage and processing have paved the way for some of the most innovative Machine Learning solutions that utilize these Machine Learning methodologies. Let us take a look at what those are:
How Machine Learning Transforms Industries
1. Self-Driving Cars
Sensor-based self-driving cars have quickly displaced human drivers. Machine Learning algorithms are being applied to advance driver assistance systems in self-driving cars. It uses a camera-based system to detect and classify objects. This helps the driver perceive and understand the world in and around the vehicle. These sensors help the drivers monitor blind spots easily, issue warning related to collisions, and also use ultrasonic radars and sensors that further help in navigation.
These sensors are also able to fetch data on traffic, surrounding weather conditions, and vehicles adjacent to them. This data is further analyzed and interpreted to make decisions based on the best scores of action.
2. Google Maps
Google Maps predicts traffic very accurately. If you own an Android phone or an iPhone with Google Maps installed and its services enabled, you know how accurately Google predicts the traffic around you.
This is because Google uses a Machine Learning algorithm to anonymously gather real-time data through your phone’s app to calculate the number of cars on the road and how fast they move. If there is more traffic on the route, it will also suggest a faster route for you.
3. Image and Speech Recognition
If you have used Facebook, you must be familiar with the automatic friend tagging suggestion it offers you through the face detection feature. How Facebook does this is very simple: Machine Learning based face detection and recognition algorithms.
Image recognition is one of the most common Machine Learning applications that is being used to identify objects, persons, places, and digital images.
Another interesting application of Machine Learning is speech recognition. Every time you use the “search by voice” feature in Google or use speech assistants such as Google, Siri, Alexa, Cortana, etc., you are actually applying Machine Learning enabled speech recognition.
Speech Recognition uses a Machine Learning algorithm that is used to convert speech to text, also known as computer speech recognition.
4. Product Recommendations
A lot of eCommerce and entertainment companies use Machine Learning solutions to recommend products to the user. Some popular examples of it would be Netflix and Amazon.
Whenever you look for a product on Amazon’s website, you immediately start receiving recommendations for the same product on every site you browse or visit. Similarly, based on your watch history and the current shows that you’re watching, Netflix recommends shows and movies that might interest you.
These sites use a Machine Learning algorithm to understand and gather data on user interest, interactions, history, etc. to customize product recommendations based on individual users.
5. Medical Diagnosis
A lot of physicians use Machine Learning powered speech recognition chatbots to assist them in conducting disease diagnoses and determining symptoms among patients.
Machine Learning is used in formulating treatment options, analyzing bodily fluids, and other multiple diagnosis procedures to assist doctors and physicians in recommending treatment options.
Oncologists and Pathologists use Machine Learning to detect cancerous tissue in patients to identify early signs of cancer.
Facial recognition software powered by Machine Learning helps in scanning patient photos and identifying phenotypes that can be helpful in discovering and detecting rare genetic diseases.
6. Fraud Detection and Prevention
Online transactions are prone to risks of fraud. And it consumes both time and resources on the part of the organization to investigate each of these frauds.
Machine Learning models can help enhance online transaction security to detect fraudulent behavior.
These Machine Learning models are trained using historical data, enabling them to identify and detect abnormalities in transactions. They are able to analyze large data sets to detect patterns that humans may have overlooked. This helps organizations achieve speed and accuracy while detecting fraud.
7. Predictive Analysis
Predictive analysis involves the use of measurable variables to predict consumer buying habits when a machine will need maintenance and forecast sales.
Machine Learning algorithms are used to build predictive models that can classify data, which are further classified according to the predefined values that analysts set. Predictive models are trained on past data to identify patterns and develop prediction models based on those patterns.
Predictive analytics finds many use cases every day such as predicting whether a certain transaction is genuine or fraudulent, calculating the possibility of a fault by improving the prediction system, and forecasting what a particular consumer is likely to buy, among various other things.
Predictive analytics can be used for everything from developing products to real estate pricing.
8. Spam Detection in Emails
You must be familiar with Gmail labeling unimportant or fraudulent emails in your inbox as spam. This spam detection feature makes use of filters that work on Machine Learning algorithms. These filters are able to detect potential threats and malicious senders based on the sender’s history.
Google employs Machine Learning models and AI-driven filters that function based on user feedback and through filters like IP address, domains, and subdomains, whether the bulk senders have been authenticated, and inputs received by users, etc.
With the help of these filters, these models are able to detect and identify patterns in large data sets which helps them adapt to changing algorithms of fraudulent mail. These algorithms are updated on a regular basis based on new threats detected.
Additionally, these algorithms are able to identify whenever a certain user marks the mail as “spam” and learn from these user actions to classify emails from that particular flagged sender as spam in the future.
9. Customer Service Automation
Businesses nowadays have to manage a lot of customers online, leading to an increase in customer interactions. Handling multiple customers on a daily basis creates customer support staff shortages.
Machine Learning algorithms are employed to fill this gap through automated chatbots and similar systems. This allows businesses to automate their routine and low-priority tasks, which frees up their staff and employees to conduct more important tasks.
Moreover, these customer service chatbots are trained in human languages and variations, so they can detect speech and translate it to provide customers with an intelligent and relevant response.
10. Self-Service at Airports
Contactless technologies have seen a rise since the COVID-19 pandemic. Self-service check-in kiosk systems were implemented at Airports to automate the customer journey and promote contactless customer service.
These automated systems are developed using Machine Learning models that use biometrics technology and scanners to recognize and verify passengers at security check-ins.
11. Online Payments
You must have used an online portal at least once in your life to make a payment. Applications like Gpay and Paytm are commonly used to make payments online, which raises a concern regarding their security as it can get difficult to determine which payments are legitimate and which aren’t.
These companies have adopted Machine Learning and Deep Learning algorithms that are able to detect fraudulent transactions to enhance their cybersecurity.
Transform Your Business With Machine Learning Solutions
The modern business landscape continues to be revolutionized by Machine Learning. Machine Learning has introduced new ways of analyzing and processing data and has entirely transformed businesses’ operations. Businesses have increased their efficiency and decision-making capabilities through automation and prediction-based models.
Based on the instances cited above, it is evident that Machine Learning algorithms have revolutionized how every industry works; from Healthcare to Finance to Aviation to Automotive.
Enterprises and businesses that are yet to adopt Machine Learning models will lose their competitive advantage. To optimize their organization’s workflow, processes and streamline operations, businesses need to develop custom software that uses the latest technologies to deliver a competitive advantage to their organization.
Quixom is a leading Python-based custom software development company. Our developers leverage the latest Python technology and frameworks to develop transformational Machine Learning solutions to upscale the organization’s logistics, decision-making, analytics, compliance, and overall efficiency.
