Websites using machine learning are becoming more and more popular as artificial intelligence libraries develop.
If you’re considering your next website to utilise machine learning, or perhaps just wanting to know some general AI use cases or best approaches for websites, then consider taking notes on the below.
Different types of machine learning technologies
There are various machine learning technologies which developers are working on, these are often referred to as libraries of code. Depending on how much usage or data sets your web application is looking to handle, different code libraries.
Based on the Google Trends data, Python seems to be the most popular. This is because Python is easy to learn, intuitive and human-friendly when it comes to reading code.
For machine learning processes which only require small data sets and little usage, PHP libraries are also an option. These PHP libraries are usually the fastest to implement on standard web servers too.
Machine learning uses within websites
Throughout the development of machine learning libraries, lots of machine learning use cases are now more readily available to implement within websites. Below is a list of the most popular use cases you can use AI for within websites.
As you can imagine within the financial industry, forecasting predictions is one of the most popular uses of machine learning — by far. You can usually see the use of these first hand within stock market websites.
Forecast predictions within machine learning usually work by having previous data (referred to as data-sets). For example, you might collate data into days ranging from 1–13, which will allow you to forecast day-14.
Data filtering (spam filters)
If you have an email account, the likelihood is that your email’s server already has machine learning capabilities built-in. For instance, if John Doe sends an email across all of the email addresses on the same server, software can often learn and detect it as spam, so that the next time it happens, John will automatically be in the spam folder.
Top e-commerce websites often use this machine learning approach to generate more sales through their related product suggestions. The way this usually works is that the website analyses the key metrics of the products you’ve recently viewed or purchased, and displays them as suggestions. The more people that purchase the exact same things, the more relevant the suggestions become.
Larger digital organisations which handle large amounts of personal data (social media companies and banks, for example) often use machine learning to detect fraudulent actives.
There are multiple ways to detect fraudulent activities, however, one method is to constantly monitor your recent login history. For example, if you logged in ten times from London, but then logged in once from the US, machine learning processes can usually detect the anomaly and prompt the US login with more authentication requirements.
Whenever you use a chatbot, notice that it’ll nearly always ask you if you found the automatic answer-response helpful. This is another method of machine learning.
The way chatbot machine learning processes work is that the next time someone asks a question, the chatbot will compare it to previously asked questions, and finally provide the same answer based on the previously provided feedback left by others. The more data it has, the more relevant answers it can supply.