Coined by Arthur Samuel in 1959, machine learning (ML) is defined as the ability of a machine or a software program to operate and learn from the data without any explicit programming.
In the past decade, it has given us robotics, speech recognition, computer vision, effective web search, and self-driven cars.
Over the years, machine learning has evolved to being used in many varied fields, so much that you might even be using it without realising it. But did you know that it can be used in web development as well?
Several analysts believe that ML algorithms will entirely alter the development process for web applications.
If you’re a web developer and want to gain more competency, you can opt for machine learning courses to help you achieve more expertise in your field.
Modern web designing is getting more complicated and user-centric with each passing day. With all the recent advancements in the field of web development, it becomes difficult for developers to deliver a website that is at par with its competitors.
Machine learning tools offer a respite by providing quick and efficient solutions to problems like integration, user experience, site responsiveness, performance, and security.
Hence, developers are now resorting to machine learning tools to make their websites and web applications more competent and user-friendly.
Still not convinced?
Following are the ways in which machine learning tools are transforming modern web development:
An Assortment of Machine Learning APIs:
There are several open source and commercial ML Application Program Interfaces (APIs) that can help web application developers with their precise needs.
These APIs make the web developer’s job easier through an efficient and smooth implementation of machine learning algorithms.
For instance, there are distinct APIs that can be integrated into the web applications to automate all the processes through AI bots.
Similarly, machine learning APIs can be used by web stores to adjust product prices conforming to current demand.
The prices can later be increased or decreased if the demand rises or falls. All of this, without any explicit programming by the web developer.
Erase Security Threat
Machine learning can be used by programmers to protect web applications from emerging security threats.
The developers can design ML algorithms to identify malicious and phishing websites based on criteria of previous attacks and certain security checkpoints.
Multiple machine learning algorithms can also be combined to make the security checking processes proactive and consistent.
Furthermore, a number of online security firms are using machine learning technologies to identify malicious websites.
These tools use logistic regression to evaluate thousands of websites against a checklist of possible threats.
Similarly, a classification algorithm is also used to detect and predict phishing against criterions like domain identity, data encryption techniques, and security features.
ML-Based Design Applications
Several web design applications can help web developers to design highly efficient sites without extra costs.
These applications use machine learning and artificial intelligence to provide practical solutions regarding data gathering and pattern determination.
For example, Wix, a web development platform, is a cloud-based app that asks the web developers about their design and user requirements and creates a website based on the answers.
Another web AI based web development tool that uses machine learning algorithms is Firedrop’s Sasha. Specialised in landing pages, Sasha makes website design easy for small business owners.
Understand User Behaviour
Web applications can not only be used to create attractive websites, but they can now also use machine learning to boost customer engagement by using ML algorithms to understand customer behaviour.
E-commerce applications like Amazon and Flipkart use machine learning to monitor and understand customer conversations with their product.
Not only this, but ML algorithms can also be used to apprehend the features and functionalities that the customers are most likely to expect.
Machine learning can now be used in web development to communicate with visitors through surveys and forms.
ML algorithms, hence, can analyse customer queries, and transfer it to the relevant team, without the need of any intervention by the web developer.
This allows developers to enhance the customer experience by resolving the issues faster.
Delivery of Personalized Content
Machine learning can be combined with statistical analysis and predictive analytics to detect patterns on the website usage data.
The ML tools study trends to deliver content that is most relevant and meaningful to the users.
Web developers can, therefore, embed these machine learning technologies in their web applications to provide personalised content and information to users that would cater to their personal preferences and choices.
Facebook makes use of similar tools to detect usage patterns in their users. It then personalises news feed based on these patterns.
Machine learning can be actively used in the development of e-commerce web applications.
Web developers actively use ML algorithms to provide faster search results to the customers.
They use targeted machine learning tools to provide relevant and personalized information to users.
ML tools can thus not only be used to help customers find their products faster, but they also help them to choose products that cater to their exact needs.
Further advancements in this niche of machine learning in web development can be used to provide users with only relevant products to browse through the catalogue.
Companies like Apple, Google, and Microsoft also incorporate the use of machine learning algorithms to deliver efficient search results to their users.
Machine learning is changing the way modern websites and applications are being developed.
The developers can now embed machine learning algorithms and APIs in their web applications to deliver a personalised and user-centric experience.
Although the impact of ML tools can vary from one web application to another, web developers can effectively use several ML algorithms for their particular needs.
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