Data science comes closer to SEO every day.
Data science, and more exactly artificial intelligence, isn’t new, but it has become trendy in our industry over the past few years.
In this article, I will briefly introduce the main concepts of data science through machine learning and also answer the following questions:
- When can data science be used in SEO?
- Is data science just a buzzword in the industry?
- How and why should it be used?
A Brief Introduction to Data Science
Data science crosses paths with both big data and artificial intelligence when it comes to analyzing and processing data known as datasets.
Google Trends does a pretty good job of illustrating that data science, as a subject of intent, has been increasing over the years since 2004.
The user intent for “machine learning” has been increasing as well, and is one of the most popular search queries.
This is also one of the two ways for operating artificial intelligence and what this article will focus on.
What Is the Concrete Relationship Between Artificial Intelligence & Google?
Back in 2011, Google created Google Brain, a team dedicated to artificial intelligence.
The main objective of Google Brain is to transform Google’s products from the inside and to use artificial intelligence to make them “faster, smarter and more useful.”
We easily understand that the search engine is their most powerful tool and considering its market share (95% of users use Google as their main search engine), it comes as no surprise that artificial intelligence is being used to improve the quality of the search engine.
What Is Machine Learning?
Machine learning is one of the two types of learning that powers artificial intelligence.
Machine learning tends to solve a problem through a frame of reference and the output is checked by a human being, as it always comes with a certain percentage of error.
Google explains machine learning as follows:
“A program or system that builds (trains) a predictive model from input data. The system uses the learned model to make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model. Machine learning also refers to the field of study concerned with these programs or systems.”
More simply, machine learning algorithms receive training data.
In the example below, this training data is photos of cats and dogs.
Then, the algorithm trains itself in order to understand and identify the different patterns.
The more the algorithm is trained, the better the accuracy of the results will be.
Then, if you ask the model to classify a new picture, you will obtain the proper answer.
Google Images is certainly the best example to reproduce this explanation. – Read more