The applications of machine learning, as an important part of technology, are evolving rapidly. We use machine learning in our daily lives unconsciously, such as Google Maps, Google Assistant, Alexa, and so on. The need for machine learning engineers is in high demand due to evolving technology and the production of huge volumes of data called Big Data. On average, an ML engineer can earn 111,490 USD. Therefore, in this article, we want to introduce you to a number of applications of machine learning. You can also access the machine learning article.
There are various virtual personal assistants such as Google Assistant, Alexa, Cortana, Siri. As the name implies, they help us find information using voice training. These assistants can only help us in various ways with voice instructions such as playing music, calling someone, opening emails, scheduling appointments, and so on.
Recently, personal assistants have been used in chat bots, which are implemented in various food ordering programs, online educational websites, as well as in travel programs. Machine learning is an important part of these personal assistants because they collect and modify information based on their previous involvement with users. Later, this data set is used to provide results that are in line with users’ preferences.
.Traffic forecasting: We have all used GPS navigation services. As we do this, our current locations and speeds are stored on a central server for traffic management. This data is then used to map current traffic. Machine learning in such scenarios helps to estimate areas where congestion can be found based on daily experiences.
Businesses also turn to machine learning, deep learning, and neural networks (a set of algorithms designed to detect patterns) to help them understand images. Image recognition is one of the most common applications of machine learning that is used to identify objects, people, places, digital images and so on. A common use of image recognition is to suggest automatic tagging of friends.
Facebook uses face recognition to automatically find the face of a person who matches its database, and therefore suggests that we tag that person based on DeepFace.
The Facebook DeepFace project is responsible for recognizing faces and identifying people in the image. It also provides Alt tags (alternative tags) for images that have already been uploaded to Facebook.
Machine learning is proving its potential to make cyberspace a safe place, and tracking online money scams is one example. Fraud detection is one of the most essential applications of machine learning. Whenever a customer makes a transaction – the machine learning model completely photographs his profile to look for suspicious patterns.
For example: Paypal uses ML to protect against money laundering. The company uses a set of tools to help them compare millions of ongoing transactions and differentiate between buyer and seller. In machine learning, problems such as fraud detection are often considered classification problems.
Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc. to recommend a product to the user. Suppose you review a product on Amazon, but then you will not buy it. But the next day, you’re watching videos on YouTube and suddenly you see an ad for the same thing. You switch to Facebook, you see the same ad there. So how does this happen?
Well, this is because Google tracks your search history and recommends ads based on your search history. This is one of the most interesting machine learning programs. In fact, 35% of Amazon’s revenue comes from product recommendations.
Likewise, when we use Netflix, we find recommendations for entertaining series, movies, etc., and this is done with the help of machine learning.