Real-time machine learning applications

Original article can be found here (source): Deep Learning on Medium

Real-time machine learning applications

Machine learning is one technological invention that has helped man not only improve many industrial and technical processes but also advance everyday living. It is a growing technology and finds its way to several real-time scenarios. I want to talk in this article about machine learning and its real-time application. When you know the real world Machine Learning application you will use it for better results. Let us get into the subject, let’s talk about machine learning briefly.

What is Machine Learning?

It is a branch of artificial intelligence, which focuses on using statistical techniques to create smart computer systems to learn from available databases. We are currently using machine learning in a number of fields and industries. Based on machine learning algorithms, intelligent systems have the ability to learn from experience or historical data. Requirements for machine learning produce based on past experience. In this article, we’ll be addressing 10 real-life examples of how machine learning helps to create better technologies to drive the ideas of today.

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Image identification

The recognition of images is a common machine learning application. There are several cases where you identify the object as a digital image. For instance, in the case of a black and white image one of the measurements is the intensity of each pixel. Growing pixel offers 3 intensity measurements in three different colors in colored images-red, green and blue (RGB).

Machine Learning application:

We use Machine Learning to detect faces in a picture. For a multiple people database there is a separate category for each person. We also use Machine learning to both handwritten and typed letters for character recognition. We may divide a piece of writing into smaller images with a single character in each.

Speech Recognition

Speech recognition is the conversion into the text of spoken words. This is known as the interpretation of machine voice, or automated speech recognition. A software program here will identify the words spoken in an audio clip or film, and then transform the audio to a text file afterwards. For this case, the calculation may be a series of numbers describing the speech signal. We can also segment the voice signal in different time-frequency bands by the intensities.

Machine Learning Application:

In applications such as voice user interface, voice searches and more, we use voice recognition. Voice-user interfaces provide voice dialing, call routing, and control of appliances. A basic data entry and the elaboration of formal documents may also be used.

Medical diagnosis

We use machine learning in the techniques and software that help with disease diagnosis. We do use this for the clinical parameter review and their combination for the prognosis.

Machine learning application:

For example, prediction of progression of disease to derive medical knowledge for testing. This is helpful for organizing treatment and supervising patients. These are the positive realizations of the methods of machine learning. It will help to incorporate computer-based systems into the healthcare industry.

Finance:

In finance, arbitrage refers to the short-term, automated trading techniques involving a large number of securities. The consumer focuses on applying the trading algorithm for a collection of securities based on quantities such as historical correlations and the general economic variables in such strategies.

Machine Learning Application:

To achieve an index arbitrage strategy, we apply Machine learning methods. We apply linear regression and Help Vector Machine to stock flux values.

Learning associations:

The process of gaining insights into the various associations between the goods is learning associations. A good example is how to connect the unrelated items to each other.

Application for machine learning:

One of the machine learning applications is the study of the connections between the goods that people purchase. If an individual buys a product, similar products will be seen, as there is a connection between the two. Once we launch some new items on the market, they are related to the old ones in order to increase their sales. for more information learn Machine learning Course

Traffic alert (Maps)

Google Maps is perhaps the tool we use today if we go out and need traffic and directions assistance. The other day you’re traveling to another place, taking the expressway, and Maps suggests: “Because of the heavy traffic, you’re on the fastest route.” If you’ve ever wondered how Google knows, the answer is Machine learning.

Google-maps-machine-learning-application:

Yeah, it’s a combination of people who actually use the program, historical data collected from that route over time and a few tricks learned from other companies. Everyone using maps provides their location, average speed, the direction they’re driving along, which in turn helps Google collect massive traffic data, helping them predict the traffic ahead and change the route accordingly.

Social Media (Facebook)

Suggestions are one of the most common machine learning applications in Facebook or any other social networking platform.

Facebook Machine Learning application:

Facebook currently has two Machine Learning features.

Machine Learning application in Fb suggestions

Facebook uses face detection and image recognition to automatically identify the face of the person matching it’s Database and therefore suggests labeling the person based on DeepFace

Machine Learning application in Fb- faces

Facebook is responsible for recognizing the faces and identifying the person is in the picture. This also makes use of Alt Tags (Alternative Tags) for images already posted on facebook. For instance, if we inspect the image below on Facebook, a description of the alt-tag is available.

Transportation and Commuting (Uber)

If you have used a taxi booking app, you are probably using Machine Learning to some degree. This makes for a special personalized setting. Automatically senses your location and provides choices based on your context and preferences to either go home or school, or some other frequent place.

Machine learning application in Uber

Use a machine learning algorithm built on top of Historic Trip Data to improve the accuracy of ETA predictions. Through a machine learning application, they saw 26 per cent accuracy in Delivery and Pickup.

Products Recommendations

Say you are looking for an item on Amazon but you don’t purchase it here and there. But the next day you watch videos on YouTube and you will all of a sudden see an ad for the same thing. You turn to Facebook, and there you see the same ad too. This is possible with machine learning.

Machine Learning application in Product recommendations:

Well, that’s because Google is tracking your search history and providing ads based on search history. That is one of the best applications of Machine Learning. Product reviews actually produce 35 percent of Amazon’s revenue.

Virtual Personal Assistants

As the name suggests, Virtual Personal Assistants help in finding useful information when requested through text or voice. Some of the big Machine Learning applications here are:

Speech Recognition Processing Virtual-Assistants-Tools-of-Machine-Learning :

This machine learning application involves text to speech translation.

Text to Speech Translation

All you need to do is ask a basic question like “What’s my schedule for tomorrow?” “Or ‘show my flights to come.’ To answer, your personal assistant is looking for information or calling back your relevant questions to collect answers. Personal assistants are being used lately in Chatbots, in different food ordering applications, online training platforms and even in commuting apps.

Self driving cars

Yeah, here’s one of the best applications for machine learning. People are already using it. Machine learning plays a very important role in Self Driving Cars and I am sure you guys may have learned about Tesla. NVIDIA, a hardware manufacturer focused on the Unsupervised Learning Algorithm, is driving the leader in this field and its emerging Artificial Intelligence technology.

Machine learning application in self driving cars:

NVIDIA stated that Tesla-applications-of-machine-learning did not train their model to detect individuals or any entity as such. Deep Learning-based platform, and it’s crowding data from all its vehicles and drivers. This uses internal and external sensors which are IOT-shaped.

Dynamic Pricing

An ancient problem in economic theory is setting the right price for a service or a good. There is a vast array of pricing strategies that depend on the desired goal. Everything is dynamically priced, whether it’s a film rental, a plane ticket or taxi fares. In recent years, artificial intelligence has allowed pricing solutions to track purchasing trends and make decisions on more competitive prices for products.

Machine Learning application in Dynamic-Pricing

Uber’s key machine-learning solutions come in the form of surge pricing, a type of machine-learning called “Geosurge.” If you get late for a meeting and have to book an Uber in a busy area, be prepared to pay twice the normal fare. When you’re traveling only for flights in the holiday season, the chances are fares will be twice the original price.

Conclusion:

By reading this article, you may reach to a conclusion about a machine learning application. You can learn more about machine learning through Machine Learning online training.