The document discusses the integration of machine learning (ML) into SEO practices, highlighting its ability to automate tasks such as image optimization and meta description writing. It breaks down ML concepts, tools, and resources while emphasizing the importance of understanding and applying these technologies for SEO effectiveness. Additional insights include common ML models, potential SEO opportunities, and practical steps for building ML models.

1. Britney Muller | @BritneyMuller | #TechSEOBoost
Machine Learning
For SEOs
–
Predict, Automate & Transcribe

2. Britney Muller | @BritneyMuller | #TechSEOBoost
1. It already effects the work that you do.
2. You should be able to speak intelligently about it.
3. Level up by adding it to your arsenal.
Why You Should Care About ML:
–

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bit.ly/tf-for-poets

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65% Probability this is Rand!

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Automated Image Optimization

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ML is everywhere!

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Smart Compose

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1.Let’s break down Machine Learning
1.How can you apply ML to SEO
1.Tools & Resources

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What is Machine Learning?
Machine Learning is a subset of AI that combines statistics &
programming to give computers the ability to “learn” without
explicitly being programmed.

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Supervised vs. Unsupervised

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Three Common Models:
Home size vs selling
price
Duck or Snake?Animal Types

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But, how do ML models get smarter?

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The Loss Function:

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Overfitting is a common problem:

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If Machine Learning was a car,
data would be the fuel

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1.Let’s break down Machine Learning
1.How can you apply ML to SEO
1.Tools & Resources

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32. Britney Muller | @BritneyMuller | #TechSEOBoost

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Writing Meta Descriptions Sucks

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Can you believe?!
Auto Generated
Google Generated

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@jroakes @GraysonParks
Grayson Parks
Writer, programmer, constant learner.
Digital marketer, husband, golden retriever owner.
Words and data are my
Passions. #SEO @AdaptPartners
GraysonParks.com
JR Oakes
Hacker, Technical SEO, NC State fan, co-
organizer
Of Raleigh & RTP Meetups, as well as Search
Engine Land author
codeseo.io

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1. Assist with deploying AWS Lambda. --Several steps will affect the cost & security.
2. Extract the content of the webpage using the library Goose3 (a Python library w/BeautifulSoup).
3. Summarize the content using summa (or another summarizing library/model)
4. Create a Lambda Function.
a. Package the files for AWS Lambda & install the dependencies (in this case Goose3 and
summa, etc) into a folder along with what is called a handler file. The handler file is what
Lambda calls to run your script.
b. Here is the packaged Lambda function (including the dependencies):
https://s3.amazonaws.com/ap-lambda-functions/meta_summa.zip
5. Once the zip file is deployed to AWS as a Lambda function, you should get a URL to access the API
that looks like:
https://XXXXXXXX.execute-api.us-east-1.amazonaws.com/v1/ap_meta_descriptions
Find a developer familiar with AWS to:

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function pageDescription(url, length) {
if (typeof length == 'undefined' || !length || length < 1){
var endpoint = 'https://XXXXXXXX.execute-api.us-east-1.amazonaws.com/v1/ap_meta_descriptions?url=' + url;
}else{
var endpoint = 'https://XXXXXXXX.execute-api.us-east-1.amazonaws.com/v1/ap_meta_descriptions?url=' + url + "&len="
+ length;
}
var response = UrlFetchApp.fetch(endpoint);
var text = response.getContentText();
var data = JSON.parse(text);
if (data){
return data.meta_description
}
}
Copy & Paste like a badass in GSheets! =pageDescription(A2, 150)

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Use Text Summarization Algorithms to
Help Aid the Writing of Meta Descriptions
(GitHub Repo)

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Podcasts

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The average podcast listener consumes 7
different podcasts a week.
-https://www.podcastinsights.com/podcast-statistics/

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JSON Output example (jq to parse)

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Finding ranking opportunities
Title tag optimization
Keyword opportunity gaps
Client reports
Finding common question opportunities
Content creation
Log file analysis
Ranking predictions
Site crawl opportunities
GSC data analysis
Rich customer understanding
Traffic predictions
Ranking factor probabilities
User engagement
Other SEO Opportunities with Machine Learning:

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1.Let’s break down Machine Learning
1.How can you apply ML to SEO
1.Tools & Resources

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1. Collect & clean dataset
2. Build your model
3. Train
4. Evaluate
5. Predict
Most of the work
A few lines of code
One line
One line
One line
How to build your first ML model:

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CPU > GPU > TPU

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Google’s Machine Learning Crash Course
Google Code Labs
Colab Notebooks
Learn With Google AI
Image-net.org
Kaggle
Getting Started Resources

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Yearning Learning (free book preview
by Andre Ng)
Neural Networks & Deep Learning
Correlation vs Causation (by Dr. Pete!)
Exploring Word2Vec
The Zipf Mystery
BigML
Targeting Broad Queries in Search
Project Mosaic Books
How to eliminate bias in data driven
marketing
TensorFlow Dev Summit 2018
[videos]
NLP Sentiment Analysis
Talk 2 Books
Image-Net
The Shallowness of Google
Translate
TF-IDF
LSI
LDA
Learn Python
Massive Open Online Courses
Coursera Machine Learning
Advanced Resources

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What did we learn?

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What did we learn?
➢ Machine Learning combines statistics & programming
➢ A model is only as good as its training data
➢ The loss function helps us improve models, but overfitting
should be avoided.
➢ YOU can create a ML model today!!!
➢ ML will help scale SEO tasks & allow us to evolve as SEOs

64. Britney Muller | @BritneyMuller | #TechSEOBoost
What did we learn? Thank you!
@BritneyMuller
britney@moz.com