What are the Real-time Applications of ML (Machine Learning) – Trending10

Introduction

ML may be a complex concept for a normal person but many organizations and people now depend on ML models. These models help to manage volumes of data and provide useful insights. Therefore, there are many examples of machine learning in front of us. It is improving our quality of life and supporting quicker execution of business operations. We come across one or the other application of ML in our daily life.

Read on to learn about ML at work in our life that provides value in many ways.

Top 10 Applications of Machine Learning in Real-time

We are using ML in our daily lives without even knowing it. From Google Maps to Google Assistant, it is everywhere. Here are some most popular real-world machine-learning applications:

1. Speech Recognition

When using Google, we see the option of "Search by voice." It works on the concept of speech recognition and is a popular application of ML. It is a process that converts voice instructions into text. You can also call it "Speech to text", or "Computer speech recognition." 

Furthermore, machine learning algorithms are in use in various applications of speech recognition. ML applications in voice recognition are there in Google Assistant, Siri, Cortana, and more. They all use speech recognition technology to respond to the voice instructions.

2. Product Recommendations

It is one of the most popular and useful ML applications. Moreover, Product recommendation is one of the common features of e-commerce websites. It is an advanced application of ML techniques. Thus, with the help of ML and AI, websites can track user's behavior. It works on the basis of your previous purchases, searches, and history. Making product recommendations is one of the wisest uses of machine learning.

3. Email Spam and Malware Filtering

When you receive an email, it gets filtered automatically. It falls into categories like important, normal, and spam. Hence, when there is an important mail, you will receive it in your inbox with the important symbol. But, the spam emails will go into your spam box. The technology behind this is ML and it uses various filters like content filters, header filters, and permission filters.

4. Application of ML in Image Recognition

Image recognition is a way of detecting a feature or an object in any digital image. It is one of the most notable ML and AI techniques. So, Automatic friend tagging suggestion is also a popular use case of image recognition. Moreover, image recognition in combination with deep learning is highly useful in healthcare to help detect genetic diseases. 

5. Automation of Employee Access Control

Many organizations are now using ML algorithms to fix the level of access employees need in various areas. However, their access depends on their job profiles. One of the most interesting uses of machine learning is this application.

6. Financial accuracy

ML is a boon for the financial industry as many systems are going digital. There are many financial transactions that a human cannot monitor alone. Hence, this application of ML helps find fraudulent transactions. 

7. Online Fraud Detection

ML is also making online transactions safe and secure via fraud detection. Whenever you do any online transaction, there can be many ways that a fraudulent transaction can take place. There can be fake IDs that can steal money in the middle of a transaction. However, the Feed Forward Neural network checks whether it is a genuine or a fraudulent transaction.

8. Healthcare Efficiency and Medical Services

Healthcare sectors are now using ML algorithms to manage their services. They can also predict the waiting times in the emergency waiting rooms of hospitals. The models use key parameters to determine the staff information at different times of day. It also helps to keep patient records, chat logs from departments, and more.

9. Machine learning in education

ML in education can change the learning process positively. It also gives new tools for educational institutions. Thus, the application of ML can help track and improve students' performance and engagement. The ML-enabled personalized approach also makes education more accessible and engaging.

10. Social Media Features

Social media platforms apply ML algorithms to create appealing features. Therefore, platforms like Facebook track your activities, chats, and comments. They can also find out the time you spend on particular posts. Machine learning uses your own experience and makes suggestions for your profile.

Now, let’s discuss the challenges that arise during the implementation of ML in real-time.

Top Challenges of Machine Learning

Implementing ML in real-time poses several challenges that need to be addressed to ensure successful deployment and operation. These challenges include:
 

• Data quality and availability
• Latency and throughput
• Model complexity
• Model monitoring and maintenance
• Scalability
• Resource and cost management
• Feature engineering
• Security and Privacy

Conclusion

Popular machine learning software and technology are developing fast. Moreover, we are excited about the future applications that ML will enable. You can find more than one application of ML around you. ML is a hot topic in technology, and for good reason. It shows a major advancement in the capacity of modern computers to learn.

Furthermore, the rapid advancement of technology and the massive production of data puts a great demand on ML engineers. Join the Data science and machine learning course by The IoT Academy and see the difference in your knowledge.

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