Google has evolved and redesigned the way users connect to the right content they're looking for. They have made great strides towards this goal using Google AI and the BERT algorithm. However, the search Engine has not commented on how machine learning changes the weighting of various ranking signals. Here's our view of how search engines are currently using machine learning which is listed as below:

  1. Pattern detection

Machine learning is being used by search engines to find patterns that assist them to detect spam and duplicate content. Low-quality content frequently has a lot in common, such as The presence of numerous outbound connections to irrelevant and disconnected pages. Stop words or synonyms are used. The frequency with which "spam" terms have been discovered. These patterns are easily identified and flagged by machine learning. It also analyses data from user interactions to identify new spam structures and tactics, understand new patterns, and report them successfully. Although Google still uses human quality raters, the application of machine learning to find these patterns reduces the number of man-hours required to analyze the content dramatically.

  • Natural language processing(NLP)

The way a search engine detects how similar one piece of text is to another is really important. This applies not only to the words themselves but also to their meaning and context. BERT stands for Bidirectional Encoder Representations from Transformers, and it's a natural learning processing architecture that Google uses to better grasp the context of a user's search query. This is because language is dynamic and continually growing, and when we play with it, new phrases emerge. The same word is used to denote distinct things in different contexts. Machine learning is being utilized to display more specific results for those inquiries as more individuals use and search new terms online.

  • Image search to understand them

On average, more than 1087 photographs are submitted to Instagram per second, while 4000 images are published to Facebook every second. That's a total of 100 million photos uploaded to just two big social media networks. Analyzing and categorizing so many photographs is impossible for humans, but it is a simple task for machine learning. Color and shape patterns are analyzed by machine learning and linked to any existing schema data about the image to help the search engine understand what an image is in effect. Google can categorize photos for search results in this way, as well as offer reverse image search, which allows users to search using an image rather than a word query.

  • Query  identification

The user intent for each search, such as buying (transactional), researching (informational), or finding resources, may differ (navigational). Furthermore, a single keyword may be valuable for one or more of these purposes. Machine learning can be used to determine the intent behind a user's search by analyzing click patterns and the content type with which they interact with a search engine.


These above-mentioned factors play an important role and tell us how machine learning has evolved in search engines too. These machine learning factors have helped in making search engines more user-friendly and bringing more traffic to the websites as well.