The Most Used Machine Learning Applications in Real World
Machine Learning Applications
Isn’t it true that Machine Learning has now made life a bit easier for us? Well not really, but still it has become a very important part in today’s world. This technology is an all-rounder technology. Do you the reason behind it?
The reason is that wherever it is used, that field becomes advanced. Even the devices that you use are now becoming more powerful. These devices include computers and other electronics. This Machine Learning article talks about the various applications of Machine Learning.
Here, we will be looking at various areas of research. We use these Machine Learning applications in our regular lives as well. Without these Machine Learning applications, things would be very difficult for us.
Machine Learning has now expanded to great extents. It now caters to the customers’ needs at any time. This creates a lot of demand for it in the market. So, let us have a look at some important Machine Learning applications.
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Machine Learning Applications
Machine Learning has various applications in many fields. Nowadays, we are seeing a constant growth of ML in various industries. Every area ranging from business to medical and science, ML has its influence. Image recognition, predictions, etc are general ML applications.
Wine quality measure, stock market analysis are some day-to-day ML applications. ML has now become a pioneer in technology. It focusses on many of our modern-day problems. This is why it is so popular. We can use it to any extent. Here we will be looking at some of the leading Machine Learning applications. Let’s start:
1. Medical Sector
In the medical sector, ML helps in predictions, analysis, and classification. In classification, it classifies the disease as normal or dangerous. Cancer predictions use both ML and Deep Learning. Heart monitoring, retinal scan, etc is also possible using ML.
Machine Learning can never completely automate the medical field. However, it can help the doctors in diagnosis. Any type of cancer can be detected using hospital pathology data and tumor images. ML application is great for detecting cancer.
Google has developed a new Deep Learning algorithm. This algorithm does a retinal scan (back of the eye) of the patient. Using the data, it can predict any heart diseases. It is rather unusual to scan the eye to find any heart ailments.
However, Google is still testing this algorithm. Research papers are available on this. The back of the eye consists of blood vessels. These vessels tell the person’s age, blood pressure, etc. We can also find if the person smokes or not using this. It is not yet ready and is under research.
2. Banking and Stock Market
We know that investing in the stock market can be either good or bad. Here, ML can play a major role in the prediction of stock prices. Prediction in the stock market can prevent losses largely.
The ML algorithms are based on supervised learning. The algorithm trains its model by using the previous data. This previous data includes stock prices over some time. The algorithm makes predictions of future stock prices using the training data.
Various banking areas use ML. It is now used in customer service, mobile banking, etc. It can help to detect credit card frauds. Some banks are using ML to tackle these problems. These are Bank of America, Wells Fargo, Citibank, JP Morgan, etc.
3. Speech Recognition
Speech recognition is a very popular Machine Learning application. This algorithm uses sound and linguistic models. The sound model processes the sounds it hears and filters out words. The linguistic model tries to match the sound of the word with similar words.
This helps to distinguish between similar-sounding words. Our mobile phones use speech recognition as well. The Google Assistant is also a product of speech recognition. Alexa and Google Home are devices specially made for this.
With speech recognition, you can filter out particular audios that you want. Google Translator also uses speech recognition. Speech recognition has a lot of potential in the market. It can communicate with robots too.
4. Image Recognition
Image recognition is one of the top leading Machine Learning applications. The algorithms use various classification and clustering techniques. Using this they train the model to classify between two images. This has a wide range of uses both in daily life and in special cases.
Your smartphone has Google Lens in it. Google Lens identifies objects through a camera. After that, it does a web search on them. A visual search API makes it possible. You can search for anything in an instant. Speech recognition also serves for security purposes. Phones use facial recognition as a security measure.
Image recognition can also help in interactive marketing. This is a very creative type of marketing.
In classification, we classify data under certain labelled classes. This is a type of supervised learning approach. In this, we have the training data. We just need to run it in a model. The model uses the training data to learn and improve. Every time, it runs a different dataset from the training data. This helps it to identify various patterns. The algorithm checks the similarities in patterns.
Let us take an example for a better understanding. We have a training dataset of shark and whale images. It consists of data from all species of these two categories. The algorithm identifies the same and different features of these two. Shark has gills to breathe whereas the whale does not. Shark has a vertical tail fin, a whale has horizontal. These are some features used for classification. Based on these, the model can train itself. The model remembers the patterns it has learned. In the future, it will recognize these two without any training data.
Classification is a fundamental application of ML. It is used in nearly all major sectors of industry.
Regression is one of the most important concepts of ML. It is also a very important ML application. Regression is used in finding the closest possible outcome. It is very useful in the areas of science, mathematics, etc.
Regression helps in training mathematical and statistical models. It can find the relationship between datasets and patterns. There are various types of regression. We have:
- Simple Linear Regression
- Polynomial Regression
- Logistics Regression
- Support Vector Regression
- Lasso Regression
- Ridge Regression
- Quantile Regression
These were some of the important types of regressions.
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Predictions are one of the main reasons for studying ML. Using certain data and behaviour, we can predict its future outcome. For example, we can take the example of weather and natural hazards. First, let us talk about the weather. We can analyze the weather patterns of an area. Using that data, we can predict the future weather of that place. A huge dataset of many months of weather is analyzed.
This data can now train an ML model. This model studies different types of weather patterns and their outcomes. The model learns the results obtained in training. This knowledge will be used to predict the weather of a future date. This can tell us if it will be hot, windy, hazy, etc on any day.
The next example is of a natural hazard. Let us take an earthquake. Although the prediction methods are still under research. The research is to study the earthquake-prone areas. In addition, it includes the study of previous earthquakes and their locations.
This data could at least help us to give an estimate. The estimate on where there is more chance of an earthquake. This at the moment is only under research. For now, it is impossible to predict the exact date of the earthquake. However, maybe it might be possible in the future.
Next in TechVidvan’s Machine Learning applications article, let’s see what are the day to day ML applications.
There are various day-to-day usages of ML. We can point our several examples around us. Here we will be seeing the most common ML applications. The ones which we users daily use. Let us have a look at them.
1. Search Engine
Every one of us uses the internet for some purpose every day. Most of us use the search engine Google for most of our searches. However, do you know that the Google search engine uses ML? Yes, it does. For every search you make, Google uses the keywords in your searches. With those keywords, it shows you different web pages. You can do a similar search with a different keyword. It shows you some new web pages as well. With this Google uses your search history to improve its search engine. It aims to provide even better search results after every search.
2. Chat Bot
Chatbots serve in various customer service platforms. They also serve in many flight-booking sites to assist customers. These Chatbots are trained models. The training data includes specific questions and keywords. When the customer types, the Chatbots analyses the query. Based on the keywords found, it provides related results.
3. Google Assistant, Siri and Alexa
These three are the prime examples of ML on your smartphone. They are assistants based on speech and text recognition. They are useful in many ways. You can simply use voice commands to navigate through the internet or mobile functions. You can control your phone as well with voice commands.
4. Google Lens
In the smartphone camera, there is a feature of the Google Lens. It directly connects your camera to the internet. Google Lens uses image recognition. When you take a picture from the Google Lens, it does a web search on the image. Click the picture of a laptop. It will run a web search. It will then show various laptops of the same model on the internet.
In this article, we saw various Machine Learning applications. Both general applications and day-to-day applications were covered. Through this, we can understand how vast ML is. It is quickly becoming a very important part of our lives. The Internet now is an inseparable part of our lives. In the future, AI and ML will become like that.
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