Everyone’s already using examples of Machine Learning in their day-to-day lives, right from using real-time navigation to surfing the internet. It’s like they are using it in one way or the other and they don’t even know about it Artificial Intelligence and Machine Learning together are simplifying our routine operations in additional ways than one. There are a lot of amazing ways in which Machine Learning is used behind the scenes to influence our everyday lives. And business decisions and optimize operations for some of the world’s leading companies.
Machine Learning(ML) has applications in almost all fields including Healthcare, Finance, Retail, Travel, Media, etc. Here are some examples of Machine Learning that we rely on in our daily lives:
Travel And Commute
Starting right from the moment people wake up in the morning Machine learning plays its role. Most of the people use Google maps to find routes to avoid traffic and find the fastest routes to reach their respective destinations. It’s because of what we call that algorithms and learning that enable the app to offer real-time speed of the traffic movement, reducing detours, and saving our time.
Moreover, ride-sharing apps, like Uber and ola have been making optimum use of Machine Learning. For determining the pricing of our traveling, estimated time of arrival/departure, and predicting rider need to define surge price hours. Self-driving cars, bicycles on roads are one of the much-awaited commercial examples of Machine Learning and Artificial Intelligence combined. We would be noticing Machine Learning bringing change into logistics as well.
Social Media
Machine learning works on a simple concept: understanding with experiences offering the most efficient means of engaging billions of social media users. From personalizing your news feed to better ads targeting, offering personalized news feed, suggesting friends based on the users’ existing friend list, and interests. Filtering out inappropriate content and deleting offensive comments, social media platforms are using Machine Learning for their own and user benefits. To provide the best experience with a user-friendly interface and optimized content. We use social media platforms every day without even realizing how heavily these sites use Machine Learning.
Online streaming platforms like Netflix, Amazon Prime, etc. predict what its customers will enjoy watching. They are also a content creator, not just a distributor, and use data to drive what content it will invest in creating. Having in the data findings, they are willing to buck convention and release multiple seasons of a new show rather than just a pilot episode. They collect this data for each subscriber they have. And use their system and a lot of Machine Learning Applications that offer them a high retention rate.
Online Fraud Detection And Spamming
This approach aims to provide Cybersecurity to users for a better experience while they surf through the World Wide Web. Machine learning is best suited for this use case as it can scan through huge amounts of transactional data. And identify if there is any unusual behavior, to protect people from any kind of Financial fraud and spam. Such systems track transaction frequency in your account, the Machine Learning model thoroughly X-rays their profile searching for suspicious patterns recognize unusual patterns to issue timely alerts by using Machine Learning Algorithms.
Each transaction a customer or user makes is analyzed in a given time frame and given a fraud-score. It represents the likelihood of the transaction is a fraud. If the fraud score is above a particular threshold. A rejection will be triggered automatically which might difficult without the application of machine learning techniques and algorithms.
Retail Services And Recommendations
Machine learning examples in retail are one of the latest trends, retailers are implementing nowadays. Companies like Amazon, Flipkart, etc. have been adopting strategies for increasing sales by creating personal connections with their customers. Using algorithms based on Machine Learning. And by creating a better online shopping experience.
Consider your day to day experience after you shopped for a product online a few days back and then you keep receiving emails for shopping suggestions. And ads for products related to that, the moment you start browsing you come across statements like “Customers who bought this product also bought” and “Customers who viewed this product also viewed”. These recommendations of products and services are the advanced applications of machine learning.
Machine Learning(ML) methods like supervised, semi-supervised, unsupervised, are used to develop these products recommendation based system. On the basis of your behavior with the website/app, past purchases, items liked or added to cart, brand preferences, etc., the recommendations are made.
Healthcare
From assistance in repetitive jobs to medication. Machine Learning examples are on their way to revolutionizing the healthcare and fitness sector. It has led to great new developments that could redefine cancer diagnosis and treatment in the future. Machine Learning can increase access to treatment in developing countries. Which don’t have enough sources to combat with any epidemic or other diseases. It can improve the sensitivity of detection, add more value in treatment decisions. And can help personalize treatment so that each patient gets the treatment that’s best suited for them.
The personalized treatment has great potential for growth. And machine learning could play a vital role in finding what kind of genetic markers and genes respond to a particular treatment also reducing the overall healthcare cost. Meanwhile, some measures are also taken in the field of Neuroscience, the foundation for Google’s DeepMind. Creating a machine that can replicate the thought processes of our own brain cells, identifying treatment plans is a technology that recommends evidence-based treatment options to physicians. It checks out for key points in the EMRs of a patient looks into its data, and identifies the best treatment plan.
Machine Learning algorithms can process massive amounts of medical images at rapid speeds. They can be trained to be extremely precise in identifying minute details in CT scans and MRIs can be applied at stages of new drug discovery. Including designing the chemical structure of drugs, target validation, investigating drug safety, and managing clinical trials. The use of Machine Learning in drug discovery will not only help to reduce the cost of introducing new drugs. But also makes the drug discovery process faster.
Manufacturing And Industrial Processes
Automated assembly, tracking, and isolating physical faults have become some of the basic applications of Machine Learning. In the manufacturing and processing industry-leading to more investment in its development. That knowledge and understanding of Machine Learning can then be used to determine how production lines can have a higher throughput of parts. It can operate at a much lower cost. And can run more efficiently thereby transforming industrial processes into a system of systems. Its applications are observed to be constrained, concentrating on explicit procedures rather than the entire assembling project or framework.
Quality control, minimizing Equipment failures, optimizing the supply chain, and inventory are some of the ways in which Machine Learning has a great impact on the industry. There are a number of other impacts that machine learning will have on smart manufacturing. And the industrial processes that make it all work. The involvement of Machine Learning can clearly lead a company to be ahead of others in the market.
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