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How Machine Learning in Search Works: Everything You Need to Know

AlsoGoogle uses machine learning in its operations to process search results. By understanding the process, you can gather an idea of how the pages of search results are expressed in their particular orders. Additionally, you can also gauge an idea of why some webpages rank higher than others on the search results page. You can always ask your next SEO agency to incorporate this into your website’s SEO strategy and ensure that your website scores a top ranking.

What is machine learning?

Machine learning allows a computer system to make decisions or offer future predictions, based on the data that is already fed to the system.  The procedure employed by machine learning takes large amounts of structured and semi-structured pieces of information to create a learning model. Once the model is perfect only then you can expect accurate results and future predictions.

Is machine learning the same as artificial intelligence (AI)?

It is extremely easy to confuse machine learning with artificial intelligence (AI), as the concepts are quite similar. However, machine learning entitles the computer system to carry out it’s learning by evaluating and understanding historical data, without being programmed directly to do so.

On the other hand, artificial intelligence (AI) is a technology that creates computer systems or programs that mimic human behavior. Artificial intelligence (AI) employs the processes of learning, reasoning, and self-correction.

While machine learning uses historical data to predict an accurate outcome, artificial intelligence solves complex problems and performs human-like tasks.

How does the model work?

The model is quite simple to understand. Typically, the model follow the ensuing sequence. The following three stages discuss the supervised model of machine learning.

Stage 1: Providing the system with a large chunk of known data

The first stage in machine learning is feeding the computer system with a significant amount of data, with a large array of variables. The information of all Positive & negative results are linked. Once the data is in the system, training is then possible. At this stage, the computer system can highlight and compare factors with historical information, so it can deliver accurate results.

To understand this better, you can take the example of your spam folder. It influences your email filter and blocks out spam messages from reaching your priority mailbox. It does this by first being provided with a large number of emails. Firstly, the machine provides the information if the email is spam (positive result) or not (negative result). Also, this allows the system to understand the difference between a spam email and an essential email. Based on this information, the computer system builds its model by identifying the similarities.

Stage 2: Offer the system a reward every time it is successful

The next stage is to offer the system a reward after every successful outcome. However, before this, the system is provided with a new chunk of data, without offering it with the information on the positive and negative results. This stage is a test that assesses the system’s accuracy. Hence, when unknown data is provided, the system must identify whether the result is positive or negative, based on the model is created.

Take the previous example into account, if the system is successfully distinguishing between spam and an essential email, it gives a reward. This reward can take the form of assigning a score value, which can be added up with each trial of success.

Stage 3: Give it autonomy

The last stage is making the machine learning system entirely independent after the success of the outcome. You always set a boundary and deadline before setting up the process. For example, Google’s email blocks 99.9 percent of all spam and phishing emails. This suggests that the machine learning system has a 0.05% chance of giving a false-positive result (sending essential email to spam).

What is the unsupervised model of machine learning?

In the unsupervised model of machine learning, the computer system isn’t informed about the elements that it is searching for. Furthermore, the system gather certain elements. Elements such as an article form a group by searching for similar elements with similar features, such as the author of the article.

How machine learning is relevant to SEO?

Understanding the system plays an important role in coming up with SEO strategies and campaigns. It is very important to understand the criteria of how Google ranks your pages on it SERPs. This way you are attentive to Google’s listings and create strategies to improve your website’s ranking. Google is using Machine learning to improve its algorithm. This is simply an area of data science that is used to improve your website SEO. Something Froztech very readily provides.

Machine learning allows your SEO agency to bring out machine-based solutions that allow them to utilize advanced data metrics and predict more realistic SEO results. It is the modern and a more high tech way to improve your website SEO. Surely, it may seem hard to adapt but once you get the hold of it, you can expect long term successful resutls.

Need a latest & improved website SEO strategy?  Contact Froztech right now and get a free quote.

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