People generally react to machine learning in one of two ways: either with a combination of fascination and terror brought on by the possibilities that lie ahead, or with looks of utter confusion and slight embarrassment at not really knowing much about it. With the advent of RankBrain, not even higher-ups at Google can tell us exactly how some things rank above others, and the impact of machine learning on SEO is only going to increase from here. Fear not: Moz's own senior SEO scientist, Britney Muller, will talk you through what you need to know.

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

3. 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:
–

4. Britney Muller | @BritneyMuller | #TechSEOBoost

5. Britney Muller | @BritneyMuller | #TechSEOBoost

6. Britney Muller | @BritneyMuller | #TechSEOBoost
bit.ly/tf-for-poets

7. Britney Muller | @BritneyMuller | #TechSEOBoost

8. Britney Muller | @BritneyMuller | #TechSEOBoost

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14. Britney Muller | @BritneyMuller | #TechSEOBoost
65% Probability this is Rand!

15. Britney Muller | @BritneyMuller | #TechSEOBoost
Automated Image Optimization

16. Britney Muller | @BritneyMuller | #TechSEOBoost
ML is everywhere!

17. Britney Muller | @BritneyMuller | #TechSEOBoost
Smart Compose

18. Britney Muller | @BritneyMuller | #TechSEOBoost
1.Let’s break down Machine Learning
1.How can you apply ML to SEO
1.Tools & Resources

19. Britney Muller | @BritneyMuller | #TechSEOBoost
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.

20. Britney Muller | @BritneyMuller | #TechSEOBoost
Supervised vs. Unsupervised

21. Britney Muller | @BritneyMuller | #TechSEOBoost

22. Britney Muller | @BritneyMuller | #TechSEOBoost
Three Common Models:

23. Britney Muller | @BritneyMuller | #TechSEOBoost
Three Common Models:
Home size vs selling
price
Duck or Snake?Animal Types

24. Britney Muller | @BritneyMuller | #TechSEOBoost

25. Britney Muller | @BritneyMuller | #TechSEOBoost
But, how do ML models get smarter?

26. Britney Muller | @BritneyMuller | #TechSEOBoost
The Loss Function:

27. Britney Muller | @BritneyMuller | #TechSEOBoost
Overfitting is a common problem:

28. Britney Muller | @BritneyMuller | #TechSEOBoost

29. Britney Muller | @BritneyMuller | #TechSEOBoost
If Machine Learning was a car,
data would be the fuel

30. Britney Muller | @BritneyMuller | #TechSEOBoost

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32. Britney Muller | @BritneyMuller | #TechSEOBoost
1.Let’s break down Machine Learning
1.How can you apply ML to SEO
1.Tools & Resources

33. Britney Muller | @BritneyMuller | #TechSEOBoost

34. Britney Muller | @BritneyMuller | #TechSEOBoost

35. Britney Muller | @BritneyMuller | #TechSEOBoost
Writing Meta Descriptions Sucks

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40. Britney Muller | @BritneyMuller | #TechSEOBoost
Can you believe?!
Auto Generated
Google Generated

41. Britney Muller | @BritneyMuller | #TechSEOBoost
@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

42. Britney Muller | @BritneyMuller | #TechSEOBoost

43. Britney Muller | @BritneyMuller | #TechSEOBoost
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:

44. Britney Muller | @BritneyMuller | #TechSEOBoost
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)

45. Britney Muller | @BritneyMuller | #TechSEOBoost

46. Britney Muller | @BritneyMuller | #TechSEOBoost
Use Text Summarization Algorithms to
Help Aid the Writing of Meta Descriptions
(GitHub Repo)

47. Britney Muller | @BritneyMuller | #TechSEOBoost
Podcasts

48. Britney Muller | @BritneyMuller | #TechSEOBoost
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)

52. Britney Muller | @BritneyMuller | #TechSEOBoost
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:

53. Britney Muller | @BritneyMuller | #TechSEOBoost
1.Let’s break down Machine Learning
1.How can you apply ML to SEO
1.Tools & Resources

54. Britney Muller | @BritneyMuller | #TechSEOBoost
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

62. Britney Muller | @BritneyMuller | #TechSEOBoost
Google’s Machine Learning Crash Course
Google Code Labs
Colab Notebooks
Learn With Google AI
Image-net.org
Kaggle
Getting Started Resources

63. Britney Muller | @BritneyMuller | #TechSEOBoost
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

64. Britney Muller | @BritneyMuller | #TechSEOBoost
What did we learn?

65. Britney Muller | @BritneyMuller | #TechSEOBoost
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

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