Machine learning has been dominating discussions on how computers are and can be used to make better decisions and execute tasks much more efficiently than humans in recent years. In fact, machine learning is leveraged in many common applications we use on our phones and personal computers without us probably thinking about how computer software is making our lives easier.
In this article, I plan to highlight six common applications of machine learning used in the systems we interact with every day.
The average user of the internet searches for everything on Google, Yahoo, and other search engines. With the immense amount of websites, pages, videos, images, and other files on the internet, it would be practically impossible to tell which relevant results to return for a particular search query. Search engines leverage machine learning algorithms to rank the importance of pages based on their content.
This is an incredibly hard task given the number of webpages, the number of search queries per second, and the growing human desire for increasingly fast response to queries on the internet. Search engines find pages and content on the internet, index the pages, and leverage machine learning algorithms to rank the pages for specific keywords.
We all know how hard it is to learn a new language, even, to an intermediate level. But we see how easily Google Translate and other language translation platforms do it. Many natural language processing techniques together with advanced deep learning algorithms are employed to translate one language to the other with minimal errors and with an incredibly fast response time. Visit nltk if you would like to know more about how computers analyse written human language.
The ability to recognise people in pictures and identify them, given a previous knowledge of the persons, is a very easy task for humans. This is however an arduous task for a computer to perform. Given the right amount of human faces to learn from, a computer can also easily recognise human faces without prior knowledge of those particular faces. A further step of verifying who a person is in a picture or in a video can also be achieved by a computer given prior knowledge of the person. Visit the Pypi face recognition project, if you would like to play around with some deep learning algorithms for facial recognition in Python.
Nowadays, machine learnings algorithms are used to help buyers on e-commerce websites to make buying decisions based on previous purchases and interests by recommending new items that might be of interest. Similarly, video content platforms such as Youtube and Netflix, suggest new videos to watch based on our previous activity on the platform. More often than not, these automatic suggestions are pretty accurate and may lead us to purchase more items or watch more videos. Without these advanced machine learning algorithms, users would have to always go through an enormous list of items in order to make a decision. This would lead to an information overload making the experience unnecessarily cumbersome.
A global economy demands the ability of people of all walks of life and languages to communicate in order to promote trade. The ability to easily recognise speech, translate to text, and possible back to a translated speech is of immense importance to the global economy.
Moreover, achieving automatic speech recognition implies the ability to watch videos and listen to talks and speeches from different regions of the world without the need for a human translator. Machine learning for speech recognition is an advancing sub-branch of artificial intelligence that we see its everyday use for automatic subtitles in videos, real-time speech translations, and speech to text.
The ability to automatically recognise the sentiments in texts, such as product reviews and comments, is very crucial for companies to automatically determine the positive and negative perceptions of products. An obvious advantage is the ability to quickly determine which products have the most favourable comments in order to expand its reach and which ones have negative reviews in order to improve the product. I have written an article on the importance of sentiment analysis how it may be leveraged using Amazon Web Services' Comprehend.
I believe this article has clearly shed light on some everyday applications of machine learning. If you have any questions or comments, please drop them in the comments section below and I will be glad to answer them.
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