1. Machine Learning:
Know Enough To Be Dangerous
Britney Muller
Senior Data Scientist @ Moz

2. 74%
of online consumers get
frustrated when content
appears that has
nothing to do with
their interests.
1. It already affects 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:
–

10. bit.ly/tf-for-poets

16. #1 MACHINE LEARNING TIP:
Find models that already do the thing you are
trying to do and feed it
YOUR data!

17. What if…
http://bit.ly/generate-text

18. After 30 Epochs

19. After 100 Epochs

20. After 200 Epochs
http://bit.ly/rand-b

21. ML is everywhere!

23. 1. Let’s break down Machine Learning
2. How to leverage ML to be a dangerous SEO
3. Tools & Resources

24. 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.

25. Supervised vs. Unsupervised

26. Three Common Models:

28. But, how do ML models get smarter?

29. The Loss Function:

30. Overfitting is a common problem:

33. 10 Year Challenge

35. https://www.macworld.com/article/3070767/googles-gboard-doesnt-send-your-keystrokes-but-it-does-leak-chicken-and-noodles.html

36. 1. Let’s break down Machine Learning
2. How to leverage ML to be a dangerous SEO
3. Tools & Resources

42. Writing Meta Descriptions

46. New Auto Generated
Previous Google Generated

47. @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
Love working with smart people on
smart things. Tech SEO Director at
@adaptpartners
codeseo.io

49. 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:

50. 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)

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

54. Automated Image Optimization

55. Podcasts

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

59. JSON Output example (jq to parse)

64. Finding ranking opportunities
Title tag optimization
Keyword opportunity gaps
Client reports
Finding common question opportunities
Content creation
Log file analysis
Parsing text into entities (insurance ex)
GSC data analysis
Rich customer understanding
Traffic predictions
Ranking factor probabilities
User engagement insights
Marketplace Creation
Other SEO Opportunities with Machine Learning:

67. 1. Let’s break down Machine Learning
2. How to leverage ML to be a dangerous SEO
3. Tools & Resources

75. CPU > GPU > FPGAs > TPU
Flexibility< ------------------------------------------- > Power

76. Machine Learning Toolkit For SEOs
Search ‘Harvard CS109’ in GitHub
Google CodeLabs – Break things!!!
Colab Notebooks OR Jupyter Notebooks
Learn With Google AI
Image-net.org (don’t forget it’s racist tho!)
Kaggle
AI For Everyone (Coursera)
Seedbank
PEAR – People + AI Research
Tensorflow.js
Getting Started Resources

77. 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
The Shallowness of Google
Translate
TF-IDF
LSI
LDA
Learn Python
Massive Open Online Courses
Coursera Machine Learning
RAY by Professors at UC
Berkeley
Advanced Resources

78. @BritneyMuller
britneym@moz.com
Thank You!