The Role of Machine Learning Applications in Our Daily Lives

The Role of Machine Learning Applications in Our Daily Lives

Did you know that machine learning applications have become a part of our day to day lives? You may not have consciously realized but these apps are in-grained in all the minutest of our daily tasks. Are you curious to know some of the machine learning application examples?

Well, this blog is all about the concept of machine learning and how it has impacted the smallest routine activities!

Machine learning helps us with right from the traffic alerts and social media to dynamic pricing and fraud detection. Some of the biggest names in the industry such as Uber, Amazon, Google, etc are using machine learning algorithms to improve user experience.

Of course, we will be discussing how these industry giants are making use of machine learning apps. As we discuss machine learning in detail here, we are pretty sure you might have some similar ideas for your business.

Don’t hesitate to reach out to an application development company for a better understanding of machine learning and its implementation.

Before we begin discussing some amazing machine learning applications, let’s understand the key elements of machine learning.

Machine Learning Introduction

Machine learning is a part of Artificial Intelligence (AI) which enables the computer algorithms to learn and interpret without being programmed to do so.

By learning from the data and information, the system is able to automatically change and improve its algorithms. The learning algorithm enables the system to identify and build predictive models on the basis of observed data. One of the goals of machine learning is to get useful predictions and not perfect ones.

Now, that you know the machine learning definition, you should know the smaller subsets of it too! The subsets are classified on the basis of whether the data fed is labeled or not. The subsets of machine learning are as follows:

Supervised Machine Learning

It is the process of machine learning where the algorithm’s inputs and their respective outputs are accurately labeled. As the input and output data are labeled, the system is trained to recognize data patterns with the help of algorithms. Due to this, the system will be able to provide accurate outputs based on the patterns.

Supervised machine learning is beneficial as it can predict outcomes without the help of humans. For instance, when social media recognizes someone after you have tagged them in a picture.

Unsupervised Machine Learning

This system is in contrast to supervised learning. The data is fed into the system, but the outcome is not labeled. Unsupervised learning is where the system determines patterns from given information on its own without being trained to do so. Once the created patterns are stored, future inputs are assigned to produce an output.

It’s advantageous because it can show data patterns that humans may not have observed. A classic example of unsupervised learning is when social media gives you recommendations for people to follow.

Reinforcement Machine Learning

Although it is mentioned as a separate category, it is a type of unsupervised learning. Just like unsupervised learning, the provided data is not labeled and the system has to create its own patterns.

However, there is a difference between them. In the case of reinforcement, when a correct output is produced, the system is told so. Such type of learning enables the system to learn from its experiences and survey the possibilities.

The perfect and relatable example of reinforcement learning is Spotify. When Spotify gives a recommendation, you can select “thumbs up” or “thumbs down”. Your selection will enable Spotify to learn your music taste.

We hope that by now you are clear about some of the machine learning facts. How about we discuss some of the current examples of machine learning applications in the industry? Here you go! Get ready to be amazed!

Machine Learning Applications:

As seen in the above discussion, you can apply machine learning in almost any industry. If you are still not convinced to hire an app developer for your project, the following examples will surely change your opinion.

1. Commute Made Easier

Uber is a household name today. Have you ever wondered the secret to its excellent quality of services? Well, its machine learning!

Whenever you book a cab using Uber, you are already using machine learning to a certain extent. It provides an entirely personalized application to you. It automatically detects your location and provides destination suggestions on the basis of your history and patterns.

You might be wondering what about the accurate ETA prediction? Uber uses a machine-learning algorithm layered on the historic trip data. Uber saw 26% accuracy in pickup and delivery with the use of machine learning.

2. Precise Traffic Alerts

Google Maps is another commonplace name today. No matter where you are heading, it has always got your back.

So, how does Google know all about the traffic predictions and providing you with the fastest route? It combines data of people currently using the service, historic data of that route and few tricks from other companies.

Everyone using Maps is providing their location, average speed and route. All this information helps Google collect massive amounts of data which helps them predict the traffic and recommend a route accordingly.

Google Maps can be considered one of the best and well-known examples of machine learning applications.

3. Social Media

Right from personalized news feeds to better ad targeting, social media platforms are using machine learning for the benefit of both parties. Here are few such examples from your social media that are nothing but its applications!

Machine learning is focused on understanding experiences. You often get recommendations to connect with certain people. These recommendations are provided on the basis of your profile visits, interests, workplace/college, etc.

You upload a picture with your friend and Facebook instantly recognizes him/her. Facebook notices the unique features and matches them with your friend list. At the backend, the entire process is complicated but at the front end, it is a simple application of machine learning.

4. Product Recommendations

How many times have you checked a product, not purchased it and seen the ads for it the next day? You switch to a different platform and there is the same ad. So, how does this happen?

All this is because Google tracks your search history and shows ads accordingly. This can be called one of the coolest applications of learning!

Fun Fact – Amazon’s 35% revenue is generated from product recommendations! 

5. At Your Virtual Assistance!

Siri and Alexa are the popular virtual personal assistants. You can command them with a text or voice and consider your task done! Right from setting reminders to telling you about the latest weather updates – these assistants have got your back!

Machine learning plays an integral role here as these devices fetch information on the basis of previous interactions. Later on, this data is utilized to provide custom responses to your queries.

6. Self Drive Cars

Tesla is the only name that comes to our mind when we hear self-drive cars. Machine learning plays a crucial role in this genre. Tesla’s current AI is driven by NVIDIA which is based on unsupervised learning algorithms.

The model functions on deep machine learning architecture that gathers data from all its vehicles and drivers. Its internal and external sensors are a part of IoT.

7. Dynamic Pricing

To help you better understand the concept, some examples of dynamic pricing are cab fares and flight tickets. Artificial intelligence is used to track the demand and provide more competitive prices.

Let’s take Uber for an instance. Uber uses a machine learning model called “Geosurge” for surge pricing. If you book a cab at peak working hours or for popular locations, be ready to pay double the fare.

If you look at it, machine learning applications can help your business sustain in this competitive market.

8. Google Translate

Gone are the times when you had to struggle with the language in a new country. Google Translate is here to your rescue!  Google’s Neural Machine Learning uses Natural Language Processing which works on thousands of languages and dictionaries and provides the most accurate translations.

Google Translate can be considered one of the most used and productive implementations of machine learning.

9. Automated Surveillance Systems

It’s not possible for a single person to monitor multiple cameras and prevent all kinds of mishaps.

This resulted in the use of AI for video surveillance systems to detect crime even before they happen. The system will detect unusual behavior patterns and alert humans to avoid mishaps. When such activities are reported correct, surveillance services can be improved.

10. Online Video Streaming

Netflix and Chill – Every millennial has heard this phrase. With over 100 million subscribers, Netflix is definitely ruling the online streaming world.

It has single-handedly managed to raise the same question in the minds of people across the globe. “How can a single website be so successful consistently?”

The answer is no surprise – Machine Learning!

Its algorithm collects a lot of data about the users. This data is collected for each subscriber and Netflix uses its Recommender System and a lot of machine learning applications. Thus, providing a personalized user experience to every subscriber.

11. Fraud Detection

With everything becoming digital, it is important to have a secure web world. Machine learning is playing its part by detecting monetary frauds online.

For example, Paypal uses ML for protection against money laundering. The company has implemented a set of tools that monitor millions of transactions and identify legitimate and illegitimate transactions.

12. Financial Services

Here, machine learning can protect companies from any financial fraud that may happen in the future. Moreover, it can provide precise guidance for future investments.

Using machine learning, cyber-surveillance protects institutions with a higher chance of financial risk.

Conclusion

All the above-mentioned examples of machine learning applications are a clear indication of how it has entered the minutest aspects of our daily lives. It can be rightly said that machine learning has not only made our lives better but also productive.

Every need can be taken care of with just voice or text commands. There is no denying the fact that the future scope of machine learning is endless.

Are you too convinced about the success of machine learning applications? Then, don’t waste your precious time to approach a web design and development team to create a beautiful application for you!

Discuss your dream project with us at [email protected] !

What is Machine Learning?

Machine learning is a part of Artificial Intelligence (AI) that enables computer algorithms to learn & interpret without being programmed to do so.

What are the examples of Machine Learning?

Image recognition, speech recognition, prediction, classification, and regression are some of the examples of machine learning.

How is machine learning useful?

It simplifies product marketing, facilitates accurate medical predictions, improves the precision of financial rules, and better customer segmentation, etc.

Which machine learning algorithm is best?

Some of the best machine learning algorithms are the Naive Bayes Classifier algorithm, K Means Clustering algorithm, Support Vector Machine algorithm, and Linear Regression.

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