Applications of Machine Learning

Like Google Maps, Google Assistant, Alexa, and more, we use machine learning in our daily life without even noticing. Here are some of the most common machine learning applications in the real world:

Image recognition

It is one of the widely used applications of machine learning and is used to display objects, places, people, digital images and more. Facebook offers a feature that automatically suggests you tag your friends. Automatically suggest tags when you upload a photo with your Facebook friends. The technology behind this is machine learning facial recognition and facial detection algorithms. It is based on a Facebook project called Deep Face, which is responsible for facial recognition and identification of people in photos.

Voice recognition

While using Google, you will see an option for Voice Search, which corresponds to voice recognition, a popular machine learning application. Speech recognition is the process of converting spoken instructions into text, also known as speech-to-text. Today, machine learning algorithms are widely used in various speech recognition applications. Google Assistant, Siri, Cortana, and Alexa use voice recognition technology to follow your voice instructions.

Traffic forecasts

If you want to go to a new place, you can get help from Google Maps, which shows you the correct route on the shortest route and predicts the traffic situation. Predict traffic situations, such as free, slow or very heavy traffic, using two methods:

• Vehicle location in real time from the app and Google Maps sensors.
• The average travel time has been the same in the last few days.

Everyone who uses Google Maps helps us improve this app. Get information from the user and send it back to the database for better performance.

Product Recommendations

Machine learning is widely used by many e-commerce and entertainment companies such as Amazon and Netflix to recommend products to users. If you search for certain products on Amazon, you will see ads for the same products while browsing the web with the same browser and this is due to machine learning. Google uses various machine learning algorithms to understand user interests and suggest products based on customer interests. Likewise, Netflix finds some recommendations for things like entertainment series and movies, again using machine learning.

Self-driving car

Self-driving cars are one of the most interesting applications of machine learning. Machine learning will play an important role in self-driving cars. The most famous automaker, Tesla, is working on self-driving cars. Unsupervised learning is used to train car models to detect people and objects while driving.

Spam and malware filter

When you receive a new email, it is automatically filtered as important, normal and spam. We always get important emails in our inbox, important codes and spam emails in our junk inbox. The technology behind this is machine learning.

Below are some of the spam filters that Gmail uses.

• Content filter
• Main filter
• General blacklist filter
• Rule-based filter
• Consent filter

Some machine learning algorithms, such as multilevel perceptrons, decision trees, and naive Bayesian classifications, are used for spam filtering and malware detection.

Virtual personal assistant

There are many virtual personal assistants like Google Assistant, Alexa, Cortana, and Siri. As the name suggests, it helps you find information using voice prompts. These assistants can help us in many ways with just voice commands, such as playing music, calling someone, opening emails and making an appointment. These virtual assistants use machine learning algorithms as a key component. The assistant records the spoken instructions, sends them through servers in the cloud, decodes them using ML algorithms and acts accordingly.

Online Fraud Detection

Machine learning makes online transactions secure by detecting fraudulent transactions. When performing some online transactions, fraudulent transactions can occur in a number of ways, including fake invoices, fake IDs, and money stolen in the middle of the transaction. A feed-forward neural network helps confirm whether it is a genuine or a fraudulent transaction.

For each actual transaction, the output is converted to a hash value and these values ​​become the input for the next round. For every genuine transaction, there are some fraudulent transaction schemes that have been modified to detect them and make online transactions safer.

Trade on the stock exchange

Machine learning is widely used in the stock market. In the stock market, there is always a risk of stock prices going up or down, so we use long-term memory machine learning neural networks to predict stock market movements.

Medical diagnosis

Medicine uses machine learning to diagnose diseases. That said, medical technology is growing very rapidly and it is possible to build 3D models that can predict the exact location of lesions in the brain. Helps to find brain tumors and other brain diseases easily.

Automatic language translation

Today it is not a problem at all to visit a new place and not know the language. This is because translating text into a known language also helps with machine learning. This functionality is provided by Google's GNMT (Google Neural Machine Translation). This is neural machine learning which translates text into a known language called machine translation.

The technology behind machine translation is the sequential learning algorithm used in image recognition and text translation from one language to another.

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