Machine learning is a form of artificial intelligence (AI) that lets an application run on algorithms. It can learn and improve its accuracy. It is used in various software applications like internet search engines, voice recognition apps on our phones, etc. While machine learning is a subset of an AI, the two are not similar. An AI is an intelligent computer that acts like a human. Machine learning is how the system develops intelligence. In this article, we give you the top applications of machine learning.
Also Read: Machine Learning: All You Need To Know
7 Machine Learning Applications
Applications of machine learning in the real world are more common than you think. Some of the most famous examples are image recognition, speech recognition, medical diagnosis, e-mail spam, malware filtering, etc. Let us go into detail about the mentioned examples and some more.
1. Image Recognition
Image recognition is a form of deep learning. It is an advanced form of machine learning which takes data as an input, analyses it, and gives an output. Different industries have different uses for image recognition that vary in the information’s accuracy level. An image is constructed of various numeric which represent the pixels. Image recognition uses the different intensities of pixels to process the information. After processing, it identifies and differentiates the details that are often hard to notice by the human eye. Image recognition is an important part of facial recognition cameras.
2. Speech Recognition
Speech recognition application converts audio files into written text. It is also known as ‘speech-to-text.’ Often confused with voice recognition, speech recognition processes the translation of human speech and converts the verbal format into a written one. The more advanced and prominent speech recognition applications use AI, machine learning, and natural language techniques to convert input into output. Human speech is a challenging format to process. Speech recognition applications are one of the most complex forms of computer science. This application uses voice assistants like Google assistant, Siri, Alexa, and Cortana.
3. Medical Diagnosis
In the healthcare system, machine learning can be used to diagnose various types of diseases. It substantially lowers the risk of an incorrect diagnosis. Medical diagnosis machine learning applications run various algorithms to collect data about possible diseases and their solutions which helps physicians make decisions. Machine learning shortens this process and helps in giving quick and efficient solutions. Some of the medical applications include virus outbreak prediction, drug development, organised health records, improved radiotherapy, and clinical trials. There are various challenges in using machine learning in the medical field, but it is an important form of technology that has proven to be very efficient.
4. E-mail Spam and Malware Filtering
Machine learning is widely used to detect spam and malware from e-mails and filter them. Phishing mailers work continuously to change their patterns to avoid being seen as spam. Machine learning technologies use various filtration techniques to detect spam and malware from essential e-mails. These techniques are:
Filters based on the list:
This technique divides the list into spammers and real users or blocks the source of specific e-mails.
Filters based on content:
According to the content of the e-mails, this technique is used to determine whether the e-mail is spam or not.
Filters for block listing:
Some IP addresses are blocked according to their reputation. This technique will check the source, block the user if the e-mail is suspicious, and mark it as spam.
Filters for headers:
This technique looks into the e-mail’s headers and determines where it is routing its information from. If anything looks suspicious, it sends the e-mail to the spam folder.
Filters based on custom rules:
According to your spam history, you can customize your rules for the spam filters.
5. Virtual Personal Assistant
A virtual assistant application uses Natural Language Processing to recognise natural languages and voice commands and completes the user’s tasks. Virtual personal assistants like Google Assistant, Alexa, Siri, and Cortana require devices connected to the internet. These applications use machine learning algorithms to provide better experiences to the users. Different applications have different protocols when it comes to the algorithm. The assistants detect the user’s voice, send it to a server cloud to get processed, run machine learning algorithms, and act accordingly.
6. Online Fraud Detection
Online transactions, if done correctly, are usually safe. But a few fraudulent accounts are waiting to scam people. Fraud detection using machine learning uses models and examples of credit card data to train the application to notice patterns often used by fraudulent accounts. The self-learning technology enables the ML application to adapt the ever-changing patterns. It detects fraudulent transactions and prevents us from getting scammed. Machine learning algorithms can detect patterns that might seem insignificant to a human and process that information faster than analysts.
7. Automatic Language Translation
Automatic language translation machine learning is also known as machine translation. It uses Neural Machine Learning to detect the source text’s language and convert it to another. It is one of the most difficult applications because human language does not hold a certain type of sequence. There are several translation techniques. Some of them are:
Dual Learning:
In this technique, texts are translated continuously until an accurate translation is generated.
Deliberation Networks:
This technique uses the generated translation and runs it until a polished version is finally given.
Agreement Regulation:
Machine learning algorithms read the text generated from every direction until they have detected and eradicated all errors.
Conclusion
The goal of machine learning is to train a model on the given data to provide efficient outcomes. Machine learning is being used in various applications every day. It is a self-learning program that runs algorithms and improves itself without being programmed. Over the years, ML algorithms have evolved and taken over industries, allowing them to improve their patterns based on the consumer experience. Machine learning has been around for decades, but Artificial Intelligence technology is growing and becoming increasingly popular in the modern world.
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