Real Time Applications of Machine Learning

While there are many things that could be achieved with Machine Learning, you might be wondering how you’d be benefited by learning this technology. Though everyone says that Machine Learning will take over our future, the real question that arises here is what is the present-day scope for this technology? What are the fields in which you can work in the present time which would be beneficial to you? If you’re pestered with these questions, then this article from PythonGeeks will help you out. Here, the article will guide you through the practical applications of Machine Learning.

You would agree that only learning algorithms isn’t sufficient, you need to know about the present applications and how you can implement your algorithms to develop them further. You need to know why Machine Learning algorithms are so high in demand. How this evolving technology will surge in the future. This article will guide you through all this in just a jiffy. Let’s look at some of the major applications of Machine Learning.

Applications of Machine Learning

1. Virtual Personal Assistant

As the name suggests, this boon of the technology uses Machine Learning algorithms to assist you in finding useful information over voice recognition. All you need to do is just ask for it. The questions could be anything under the sun, these assistants will help you by just instructing them.

You could ask, “How is the weather going to look like tomorrow?” “What are the flights from London from Germany for today?” “What is my schedule for today?”, and many more questions would be answered by these assistants. You can also instruct these assistants to remind you about certain tasks or set an alarm or make a phone call. These assistants can send commands to other resources like phone, to-d0 lists and many others.

Now that you know what a virtual assistant actually does, you’ll wonder what are the examples of these assistants? Well, the answer is quite simple, the most recognized names in the arena of virtual assistants are Siri, Alexa and Google Now. These assistants collect and refine information on the basis of your previous searching trends.

Later on, this refined data is processed to render the results that are tailored according to your preferences. You can form an integrated system with them, for example, Smart Speakers like Amazon Echo, or Smartphones like Samsung S8.

The Machine Learning algorithms that are useful to implement these Assistants are:

• Speech Recognition
• Natural Language Processing
• Speech to Text Conversion
• Text to Speech Conversion.

2. Predictions

One of the major revolutions in the field of Machine Learning is undoubtedly Prediction while Commuting. Be it predicting the fare of your cab ride based on your travel route, or be it predicting the traffic in your way, Prediction algorithms always come to your rescue. While the world is always facing an energy crisis, predicting the energy consumption well in advance can solve many of such problems.

The prediction algorithms process the data gathered through past user experiences and store them for future reference. By processing this data, the algorithms can predict the future needs of energy consumptions, cab fares, route optimization, and many more.

The algorithms used in such predictions are:

• Linear regression
• Support Vector Machine
• Ensemble Algorithm
• Gaussian Process Regression Algorithm

3. Product Recommendation

Have you ever experienced that for a few days you’re getting ads for products that you were looking for a few days ago? You searched for some products online and now your favorite social media apps and video streaming platforms now show you ads for that particular product? If not, you might have experienced that your favorite shopping website somehow always shows you products that match your taste. It certainly enhances your shopping experience but the real question here is, how do these websites know your preferences?

Well, this happens because these websites keep a track of your search history, and process them with Machine Learning Algorithms. In fact, Amazon generates 35% of its revenue through product recommendations.

The algorithms used to achieve these results are:

• Collaborative Filtering
• Content-Based Filtering
• Hybrid Recommendation Systems

4. Social Media

From automatically suggesting you to your mutual friends to personalizing your feed according to your preferences, Social Media platforms utilize Machine Learning algorithms to customize your experience. These amazing features are just another application of using the algorithms correctly. These algorithms keep track of your activities and on the basis of continuous learning suggest you friends and contents that are of your interest.

Platforms like Pinterest identify objects from the images that you’ve liked previously and process this extracted information for future pin recommendation. Social media platforms use image recognition to suggest your friends while tagging them. Your interaction with people on social media helps these platforms to suggest mutual friends.

The algorithms used by various social media platforms are:

• Face recognition
• Image classification
• Collaborative Filtering

5. Online Fraud Detection

Machine Learning is serving its potential by making cyberspace a secure place for transferring and tracking monetary funds. With the increase in the number of payment methods, the plethora of online fraud is also increasing. Experts estimate that nearly a fraud of $32 billion is reported in 2020.

Major payment companies are taking the help of Machine Learning to put an end to money laundering. Whenever a customer makes a transaction, the Machine Learning models thoroughly scan the activity of the transaction. Based on the users’ activity, the algorithm tends to raise an alert if any suspicious activity like the illegitimate transaction is detected. This detection is usually framed under the classification category.

Algorithms used are:

• Logistic Regression
• Decision Tree
• Neural Network

6. Spam and Malware Filtering

On any given day, an average of thousands of spam emails and fraudulent mails are sent. But it is not possible for any individual to filter out these emails according to their preferences. That’s where the power of your learning of Machine Learning will help you. The system security programs are powered by ML algorithms to filter out these mails.

Over 325,000 malware are detected daily and each one of them is almost similar to the previous one. Machine Learning algorithms are capable of detecting new malware and offering protection against them. This detection also helps you to automatically filter out spam mails and target the malware.

Algorithms implemented for this result:

• Decision Tree
• Decision Tree Induction

7. Video Surveillance

Can you imagine sitting all day long behind the monitor screen and keeping an eye on the activities recorded with the camera? Boring right? But that’s why we train Machines to do such tedious and boring jobs.

With the help of algorithms like behavior analysis, image detection, we can train the machines to detect crime on the basis of their behavior. The cameras track the behavior of people and classify them as usual or unusual. And once some unusual behavior is tracked, the system can then alert the human attendants which could then avoid any mishaps. Thus, this helps to reduce the crime rate and improves the quality of surveillance.

The algorithms used to achieve this are:

• Image Classification
• Behavior Analysis
• Sentiment Analysis

8. Search Engine Result Filtering

Customizing your search results have always been the core idea behind a better searching experience. This is what the leading search engines focus mainly and achieve so by using the Machine Learning classifications. Every time you search for something online, the algorithm at the backend keeps track of your behavior towards the search result. If your interaction lasts longer with the result page, the algorithm assumes that the results are in accordance with the query you put.

Similarly, if you tend to search for other results after visiting the first page, the algorithm assumes that the results didn’t comply with the search query. This way the algorithm helps in customizing the search results.

Algorithms applied while implementing this are:

• Pattern Matching
• Image classification

9. Stock Market Prediction

When the world is moving towards digitization, investment has become one of the major money-earning techniques. However, the chances of losing money with the ups and downs of the market also increase drastically. To ensure risk-free stock trading, machine learning models are trained to predict the trends of the market in advance.

In order to do so, the model uses the data analysis of the previous data and predicts the future trends accordingly.

Algorithms used are:

• Neural Networking
• Linear Regression
• Support Vector Machine

10. Governance and Surveillance

With the increase in numerous technologies, the number of security threats also increases. However, with machine learning, we can train and deploy models which assist humans in the mundane job of surveillance. With technological advancements like drone surveillance, image detection and numerous others, the risk of security breaches has lowered significantly.

Algorithms at work:

• Deep Learning
• Image Detection
• Behavior Analysis

11. Art and Creativity

Machine Learning has not only done the job of automating boring jobs like surveillance, tracking and manufacturing, but it has also helped in enhancing and diversifying the artistic world as well. With various tools like smart prediction of image transitions and audio recommendation, it has opened a whole new world for digital art.

Apart from this, various other models are trained in such a way that they can smartly suggest improvements in the created art form. Risk of plagiarism has also reduced drastically with the use of machine learning algorithms in the field of art.

Algorithms that play a vital role:

• Image Recognition
• Audio Recognition
• Deep Learning

12. Language Translation

One of the most heard of and seen applications of Machine Learning algorithms by far is language translation. With the increase in social connectivity, the regional boundaries have become insignificant. However, the lingual barrier still hovers us. Manually keeping track of all the languages isn’t plausible. But, by training machine learning models, one can cross the border of lingual differences.

Nowadays, the algorithm is trained in such a way that you don’t always have to manually look for the translations of any foreign language text in your desired language. Machine Learning has made this task simple and quite handy.

Conclusion

Hope that this article from PythonGeeks will help you to know the various applications of Machine Learning. You can now choose the field of your interest according to your knowledge of that topic. You can also learn algorithms according to your area of leading Machine Learning Algorithms.