Real Life Machine Learning Examples and Use Cases
Machine Learning has so many amazing applications that are being used worldwide. It is becoming a great helping hand in many sectors. The main purpose of ML is to make the work more automated. This concept of automation can be applied to any field and its uses are not just limited to computer science. Throughout the years, we have come across some amazing technologies. These technologies at some point were revolutionary. Google, Apple, IBM, Amazon, Tesla, etc., these are some of the pioneers of this particular field. They are using ML and AI in research, in the making of new technology and for many more purposes. We will be looking at these important Machine Learning examples, which are being used worldwide. We will discuss these machine learning use cases below along with the companies that are doing great in that field.
Google is working on various new fields like cancer detection, weather prediction, etc. IBM has created one of the smartest computers of all time, the IBM Watson. Watson is based on natural language processing and performs QnA. Similarly, we have companies like Tesla who are working on self-driving cars and other transportation-related technology using AI. This shows that the world is now quickly gearing up in this race for advancement and AI and ML are the main tools for this advancement.
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Machine Learning Examples in real Life
These are some of the examples of Machine Learning, which you might be using without even knowing it. So, it is going to be fun exploring them. So, let’s start.
1. Recommendation Systems
There are various online recommendation engines and systems, which we come across. These systems are used by many major platforms like Amazon, Netflix, etc. These recommendation engines run on an ML algorithm, which takes in user search results and their preferences. Using that data, the system provides related recommendations, the next time you open the platform.
In Netflix, you get the notifications for the new series. The algorithm of Netflix analyses the streaming history of all it’s users. Using that data, it recommends new series as per the choice of its millions of active users.
This same recommendation engine can help in generating advertisements. Take Amazon in this case. Suppose, you shop or just browse for some products on Amazon. The ML algorithm of Amazon analyses the search results of the user and then starts generating advertisements as recommendations. You can see Amazon ads similar to your searches on many different web pages, which you access.
2. Self-driving Cars
Transportation is going to be one of the main aspects of development in the future. Self-driving cars are a concept that some famous companies are trying to implement. These include Uber, Tesla and Google’s Waymo. These companies use various algorithms to make the system of the car learn. The algorithm trains and tests the model with every possible test case. This makes the system more intelligent and more efficient while making decisions.
Uber is using this algorithm for making driverless taxis. The Uber server would take the user’s pick-up and drop-off points, find the best route and then drive automatically. Similarly, Tesla is making energy-efficient cars with automated systems. It is providing the self-driving feature for privately owned vehicles. But, the complete self-drive feature is still being worked on in Tesla. The car can drive itself fastly and smoothly on highways. At the moment, it is not advanced enough to self-drive in city traffic.
Google’s Waymo has some similar ambitions. It is Google’s prestigious autonomous vehicle project. It needs some help from AI at the moment.
3. Education and the field of Gamified Learning
Gamified learning is a very creative and efficient way of providing education. This has a bit of an ML aspect to it. The algorithm collects data of the user’s answers. If the answers are correct, then they repeat at the end. If the answer is incorrect, then the question will repeat after a short interval of time to make sure you remember the right answer. This happens three or more times until all questions are over.
There are various apps, which work on Gamified learning. We have Duolingo, Udemy, and Magoosh, etc. In Duolingo, we have flashcards that keep popping up. This is a language-learning app. It provides the most creative way to learn a language. Magoosh is mainly for advanced English vocabulary. It helps you to learn tough words in English in a very efficient way.
4. Predicting Illness – Machine Learning Use cases in Healthcare
Predicting an illness will be very helpful for doctors to warn a patient beforehand. They can even predict if the illness is potent or not, which is indeed amazing. But, it might not be an easy job even using ML, but it can still be very helpful. In this case, the ML algorithm will first scan the patient’s body for any symptoms. If there is abnormal body functioning, it would take it as input, train the model and then give its prediction. Since there are thousands of diseases and double the number of symptoms, it might take time to get the results.
This aspect of ML can be extremely useful in case of an epidemic, a pandemic or an outbreak. This can help us to contain more people and prevent it more efficiently if the disease is contagious.
There are companies like Kensci, Deepmind, Arterys, etc., which are AI-based healthcare startups. They provide futuristic and efficient approaches to tackle a medical situation.
5. Credit Worthiness – Machine learning examples in Banking
Checking whether a client of a bank is creditworthy can be very tricky. This is very important as it will determine whether the bank will give you a loan or not. Traditional credit card companies just check whether the card is active or not and they check the card history. It becomes difficult to judge if there is no card history of the cardholder.
For this, we have various ML algorithms, which take into account the user’s financial status, past history of loan paybacks, debts, etc. Banks are already suffering huge losses in monetary terms as they have a lot of defaulters. In order to reduce these type of losses, we need an efficient ML system which can prevent any of these situations from happening. This would save a lot of money and the banks can provide more for genuine customers.
Deserve is one such credit card company that uses ML. It provides cards to mainly college students. It uses the same method using ML to check the creditworthiness of the student.
6. Ranking of Posts on Social Media
This concept is proving to be very useful for Twitter users. Twitter has this ML model, which shows the notification for all the latest tweets that are posted on twitter every day. Previously, this had a bit of a problem. Users who wanted to read older tweets were not able to do so. This happened because the ML model of twitter only allowed the user to see the latest tweets.
But this has changed now. You can get notifications for both the latest tweets as well as you can now check for any old tweets that you missed. This has been a great upgrade by twitter.
7. Computer Vision in Agriculture
Computer Vision can help the farmers to visually identify any weed growing along with the crop or any plant, which looks unhealthy. This helps them by saving a lot of their time. Also, there is very little chance of any error. It is also a lot quicker as the Computer vision technology would recognize odd-looking plants and the ML algorithm would classify it as potent or not.
This saves a lot of money for the farmers as they won’t have to throw the whole batch due to some infected plants.
Microsoft is promoting more research in ML for agriculture. It is also conducting various researches in this field. We also have, Blue river’s “see and spray” technology. This is the technology that uses computer vision.
8. Targeted Emails
This concept uses a lot of AI and ML. It is a type of marketing technique, but it more E-mail centric. The main concept is to create a strong impact. The E-mails have to be very precise, to the point and should be sent at the perfect timing.
The ML algorithm has to be aware of what time the consumer is most active on E-mail platforms. Using that result, it can forward the mail. It creates great marketing opportunities. There are various companies like Constant contact, SendinBlue, Drip, etc., which provide E-mail based marketing.
9. QnA based Platforms
We have simple QnA based platforms like Quora. Quora also works on an ML algorithm whose aim is to give the best answer possible for all the queries. The algorithm works in a variety of ways. It can check for the number of upvotes and downvotes for any answer and based on that result, it shows you the best one. It can also check your search history and try to find the most suitable answer to your query. You might have noticed this if you have the quora app. It randomly pops a notification for a search result. This might be related to any Google search as well.
10. Machine Learning Examples in Fashion Industry
Fashion Industry is a booming industry across the world. The millennials or the younger generation are much more into fashion than the older generation. Hence, we can see a lot of fashion startups around us. If it is a big startup, then online marketing would certainly benefit the startup. This is where the concept of ML in the fashion industry comes into play. The ML algorithm would take an online scan of the customer’s body dimensions and accordingly give the right sized clothes. We are always sceptical about buying clothes online just because we don’t know the right size. But now, ML is aiming to eliminate that problem.
We have certain companies like Fit Analytics, which perform the same task.
In this article, we looked at various new machine learning examples, which are being very useful around the world. We studied how the ML algorithm works, for certain cases and how it would create an impact. We also had a look at various companies, which are currently working on all these wonderful areas. There are many more machine learning applications like this, but we have discussed all the important ones which are trending in the market and we should be aware of them.
This would be all for this article. If you want to know more about machine learning, please refer to more of Techvidvan’s machine learning tutorials.