all the test conditions lead to a leaf hub. The leaf hub consists of class-labels, which vote against or in favor of the choice.
1.8 Machine Learning Applications in Daily Life
Some of the main areas where we use ML algorithms are in traffic alert systems in Google maps, social media sites like Facebook, in transportation and commuting services like Uber, Product recommendation systems, virtual personal assistant systems, self-driving cars, Google translators, online video streaming services, fraud detection, etc [13].
1.8.1 Traffic Alerts (Maps)
Nowadays, when we decide to go out and in need of assistance for directions and traffic situations on the road we have decided to travel, we usually take the help of Google maps. If in case you decided to travel to a city and decide to take the highway, and the Google traffic alert system suggested that “Even though there is heavy traffic, you are on the fastest route to your destination”, how does the system know all these things? In short, it is a combined data of people actively using the service, the previous data of the route collected over the years, and also involves some own tricks which are acquired by the company to efficiently calculate the traffic. Most of the people who are currently using the Google maps service is indirectly providing their location, speed, and the routes they are going to take in which they are traveling, which helps Google collect data about the traffic, which will help the Google map algorithm predict the traffic and recommend the best routes for future users.
1.8.2 Social Media (Facebook)
Social media applications like Facebook use ML to detect and recognize faces that are used for automatic friend tagging suggestions. The algorithm compares the detected faces with the database of pictures it already has and gives users suggestions. Facebook’s DeepFace algorithm which uses deep learning runs behind the Facebook application to recognize faces and identify the person in the picture. It also provides alternative tags to images already uploaded on Facebook.
1.8.3 Transportation and Commuting (Uber)
Transportation and commuting apps like Uber use ML to provide good services to their clients. It provides a personalized application that is unique to you, for example, it automatically detects your location and gives options either to go home or office or any other frequent places which will be purely based on your search history and patterns. The application uses a ML algorithm on top of historic data on trips to make accurate ETA predictions. There was an increase of 26% in the accuracy of delivery and pickup after implementing ML on their application.
1.8.4 Products Recommendations
This tells you how powerful is the ML recommendation systems are these days. Take for example, you liked an item on Amazon, but add it to your wish list because you cannot afford the item at the current price. Surprisingly, the day after, when you are watching videos on YouTube or some other application you encounter an ad for the item which you have wish-listed before. Even when you switch to another app, say, Facebook, you will still see the same ad on that website. This happens because Google tracks your search history and recommend ads depending on the activities you do. About 35% of Amazon’s wealth is generated by using product recommendation systems like these [18].
1.8.5 Virtual Personal Assistants
Here, virtual assistant finds some useful information when the user asks some questions via text or voice. There are many applications of ML which are being in these kinds of applications. Applications involve speech verifi-cation and identification systems, speech-text conversions, NLP, and text-to-speech conversion. The only thing you have to do is ask a simple question like, “What is my schedule for tomorrow?” or maybe “Show my upcoming booking”, then assistants search for information related to questions to collect information. Recently, chatbots use a personal assistant, which is being used in many food ordering company applications, online coaching or training sites, and also many in many transport applications [19].
1.8.6 Self-Driving Cars
This may be one of the most breath taking the implementation of ML in the modern world. Tesla uses deep learning and other algorithms to build a self-driving car. As the computation required for this is very high, we need matching hardware to run these algorithms, NVIDIA provides the necessary hardware to run these computationally expensive models.
1.8.7 Google Translate
Before when you remember the times when you go to a new place where the language used there is completely new to you and you find it difficult to communicate with the locals or find places you wanted to go, this was mainly because you could not understand what is written on the local spots. But nowadays, Google’s GNMT is a neural machine algorithm that has a dictionary of thousands of millions of words of many different languages, uses natural language processing to very efficiently and accurately translate any sentences or words. Even the tone of every sentence matters, it uses techniques like NER.
1.8.8 Online Video Streaming (Netflix)
More than a 100 million users use Netflix, and there is no doubt that it is the most-streamed web service in the whole world. Netflix application use ML algorithms which collect a massive amount of data about the users, when the user pauses, rewinds, or fast forwards. It also takes data depending on the day you watch the content, the date and time, and mainly the rating pattern and search pattern. The application collects these data from each of their users they have and use their recommendation systems and a lot of algorithms related to ML approaches.
1.8.9 Fraud Detection
Currently, online credit card fraud detection is 32 billion dollars market in 2020. That is approximately higher than the profit made by many MNC companies combined. Nowadays, the number of payment channels like a credit card, debit card, numerous wallets, UPI, and much more has increased the number of criminals. ML approaches fraud detection as a classification problem.
1.9 Conclusion
In the anticipating years, ML will embrace a significant reason in the divulgence of data from the abundance of information that is at present open in a different zone of utilization. The supervised learning strategies are developing constantly by the information researchers, which contain an enormous arrangement of algorithms. This zone has the consideration of numerous engineers and has picked up generous advancement in the most recent decade. The learning strategies accomplish magnificent execution that would have been hard to get in the earlier many years. Given the reckless development, there is a lot of room for the engineers to work effectively and to develop the SML strategies.
References
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2. Nasteski, V., An overview of the supervised machine learning methods. Horizons. B., 4, 51–62, 2017, 10.20544/HORIZONS.B.04.1.17.P05.
3. Sharma, R., Sharma, K., Khanna, A., Study of Supervised Learning and Unsupervised Learning. Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET), 8, VI, June 2020.
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