In the world of SEO, its important to understand the system youre optimizing for.

You need to understand how:

Another crucial area to understand is machine learning.

Now, the term machine learning gets thrown around a lot these days.

But how does machine learning actually impact search and SEO?

This chapter will explore everything you need to know about how search engines use machine learning.

It would be difficult to understand how search engines use machine learning without knowing what machine learning actually is.

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Lets start with the definition (provided by Stanford University in their course description for Coursera) before we move on to a practical explanation:

Machine learning is the science of getting computers to act without being explicitly programmed.

Machine learning isnt the same as Artificial Intelligence (AI), but the line is starting to get a bit blurry with the applications.

As noted above, machine learning is the science of getting computers to come to conclusions based on information but without being specifically programmed in how to accomplish said task.

AI, on the other hand, is the science behind creating systems that either have, or appear to possess, human-like intelligence and process information in a similar manner.

Think of the difference this way:

Machine learning is a system designed to solve a problem. It works mathematically to produce the solution.

The solution could be programmed specifically, or worked out by humans manually, but without this need, the solutions come much faster.

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A good example would be setting a machine off to pour through oodles of data outlining tumor size and location without programming in what its looking for. The machine would be given a list of known benign and malignant conclusions.

With this, we would then ask the system to produce a predictive model for future encounters with tumors to generate odds in advance as to which it is based on the data analyzed.

This is purely mathematical.

A few hundred mathematicians could do this but it would take them many years (assuming a very large database) and hopefully, none of them would make any errors.

Or, this same task could be accomplished with machine learning in far less time.

When Im thinking of Artificial Intelligence, on the other hand, thats when I start to think of a system that touches on the creative and thus becomes less predictable.

An artificial intelligence set on the same task may simply reference documents on the subject and pull conclusions from previous studies.

Or it may add new data into the mix.

Or may start working on a new system of electrical engine, foregoing the initial task.

It probably wont get distracted on Facebook, but you get where Im going.

The key word is intelligence.

While artificial, to meet the criteria it would have to be real thus producing variables and unknowns akin to what we encounter when we interact with others around us.

Right now what the search engines (and most scientists) are pushing to evolve is machine learning.

Google has a freecourse on it, has made its machine learning frameworkTensorFlow open source, and is makingbig investments in hardware to run it.

Basically, this is the future so its best to understand it.

While we cant possibly list (or even know) every application of machine learning going on over at the Googleplex, lets look at a couple of known examples:

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What article on machine learning at Google would be complete without mentioning their first and still highly-relevant implementation of a machine learning algorithm into search?

Thats right were talkingRankBrain.

Essentially the system was armed only with an understanding of entities (a thing or concept that is singular, unique, well-defined, and distinguishable) and tasked with producing an understanding of how those entities connect in a query to assist in better understanding the query and a set of known good answers.

These are brutally simplified explanations of both entities and RankBrain but it serves our purposes here.

So, Google gave the system some data (queries) and likely a set of known entities.

Im going to guess on the next process but logically the system would then be tasked with training itself based on the seed set of entities on how to recognize unknown entities it encounters.

The system would be pretty useless if it wasnt able to understand a new movie name, date, etc.

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Once the system had that process down and was producing satisfactory results they would have then tasked it with teaching itself how to understand the relationships between entities and what data is being implied or directly requested and seek out appropriate results in the index.

This system solves many problems that plagued Google.

The requirement to include keywords like How do I replace my S7 screen on a page about replacing one should not be necessary.

You also shouldnt have to include fix if youve included replace as, in this context, they generally imply the same thing.

RankBrain uses machine learning to:

In its first iteration, RankBrain was tested on queries Google had not encountered before. This makes perfect sense and is a great test.

If RankBrain can improve results for queries that likely werent optimized for and will involve a mix of old and new entities and services a grouping of users who were likely getting lackluster results to begin with then it should be deployed globally.

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Andit was in 2016.

Lets take a look at the two results I referenced above (and worth noting, I was writing the piece and the example and then thought to get the screen capture this is simply how it works and try it yourself it works in almost all cases where different wording implies the same thing):

Some very subtle differences in rankings with the #1 and 2 sites switching places but at its core its the same result.

Now lets look at my automotive example:

Machine learning helps Google to not just understand where there are similarities in queries, but we can also see it determining that if I need my car fixed I may need a mechanic (good call Google), whereas for replacing it I may be referring to parts or in need of governmental documentation to replace the entire thing.

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We can also see here where machine learning hasnt quite figured it all out.

When I ask it how to replace my car, I likely mean the whole thing or Id have listed the part I wanted.

But itll learn its still in its infancy.

Also, Im Canadian, so the DMV doesnt really apply.

So here weve seen an example of machine learning at play in determining query meaning, SERP layout, and possible necessary courses of action to fulfill my intent.

Not all of that is RankBrain, but its all machine learning.

If you use Gmail, or pretty much any other email system, you also are seeing machine learning at work.

According to Google, they are now blocking 99.9% of all spam and phishing emails with a false-positive rate of only 0.05%.

Theyre doing this using the same core technique give the machine learning system some data and let it go.

If one was to manually program in all the permutations that would yield a 99.9% success rate in spam filtering and adjust on the fly for new techniques it would be an onerous task if at all possible.

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When they did things this way they sat at a 97% success rate with 1% of false positive (meaning 1% of your real messages were sent to the spam folder unacceptable if it was important).

Enter machine learning set it up with all the spam messages you can positively confirm, let it build a model around what similarities they have, enter in some new messages and give it a reward for successfully selecting spam messages on its own and over time (and not a lot of it) it will learn far more signals and react far faster than a human ever could.

Set it to watch for user interactions with new email structures and when it learns that there is a new spam technique being used, add it to the mix and filter not just those emails but emails using similar techniques to the spam folder.

This article promised to be an explanation of machine learning, not just a list of examples.

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The examples, however, were necessary to illustrate a fairly easy-to-explain model.

Lets not confuse this with easy to build, just simple in what we need to know.

A common machine learning model follows the following sequence:

This model is referred to as supervised learning and if my guess is right, its the model used in the majority of the Google algorithm implementations.

Another model of machine learning is the Unsupervised Model.

To draw from the example used in a great courseover on Coursera on machine learning, this is the model used to group similar stories in Google News and one can infer that its used in other places like the identification and grouping of images containing the same or similar people in Google Images.

In this model, the system is not told what its looking for but rather simply instructed to group entities (an image, article, etc.) into groups by similar traits (the entities they contain, keywords, relationships, authors, etc.)

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Understanding what machine learning is will be crucial if you seek to understand why and how SERPs are laid out and why pages rank where they do.

Its one thing to understand an algorithmic factor which is an important thing to be sure but understanding the system in which those factors are weighted is of equal, if not greater, importance.

For example, if I was working for a company that sold cars I would pay specific attention to the lack of usable, relevant information in the SERP results to the query illustrated above.

The result is clearly not a success. Discover what content would be a success and generate it.

Pay attention to the types of content that Google feels may fulfill a users intent (post, image, news, video, shopping, featured snippet, etc.) and work to provide it.

I like to think of machine learning and its evolution equivalent to having a Google engineer sitting behind every searcher, adjusting what they see and how they see it before it is sent to their device.

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But better that engineer is connected like the Borg to every other engineer learning from global rules.

But well get more into that in our next piece on user intent.

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