Machine Learning for SEOs by Britney Muller

1. Machine Learning for SEOs
@BritneyMuller
Senior SEO Scientist

2. To do better, we must think differently

5. Machine learning
can be your laser beam!

6. You don’t have to be a Data Scientist to think
of the next brilliant ML application!

8. Let’s solve a problem together using ML
How can we help solve a customer
churn problem?

9. Problem: Customer Churn
To do this, you need to know:
1. What Machine Learning is
2. How to identify ML opportunities
3. Specific examples of ML in action
4. How to integrate ML into your thinking process [Framework]
Solution: What sort of data + model might you need to do this?

10. 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.

12. If Machine Learning was a car
data would be the fuel.

14. For example, teachers, nurses, childcare
ML doesn’t solve well for
soft/people skills:

15. Safe & effective ML requires diversity

19. Problem: Customer Churn
To do this, you need to know:
1. What Machine Learning is
2. How to identify ML opportunities
3. Specific examples of ML in action
4. How to integrate ML into your thinking process [Framework]
Solution: What sort of data + model might you need to do this?

20. Classification: Spam / Not Spam
Association: If milk is in someone’s cart, they are
80% more likely to buy bread.
Regression: Prediction – Home price based on the
number of bedrooms, bathrooms, m2 ,etc.

24. bit.ly/rand-b
@BritneyMuller

26. #TTTLIVE19

27. #TTTLIVE19

29. codelabs.developers.google.com

30. Problem: Customer Churn
To do this, you need to know:
1. What Machine Learning is
2. How to identify ML opportunities
3. Specific examples of ML in action
4. How to integrate ML into your thinking process [Framework]
Solution: What sort of data + model might you need to do this?

31. Machine Learning will free us up
to do more strategic work.

32. Source: news.efinancialcareers.com/ca-en/285249/machine-learning-and-big-data-j-p-morgan
“Machines have the ability to quickly analyze news feeds and tweets, process
earnings statements, scrape websites, and trade on these instantaneously.”

33. Video Generation

35. Automate Transcriptions

38. Machine Learning is becoming more accessible &
will free us up to work on higher level strategy.

41. Machine Learning will free us up
to do more strategic work.

42. Automatic 301 Redirects
searchwilderness.com/mozcon-2019

43. Automate Image Understanding
@BritneyMuller

47. Machine Learning is becoming more accessible &
will free us up to work on higher level strategy.

48. @BritneyMuller

49. We have only scratched the surface
title tag optimization
deduping questions (Quora, Stack Overflow)
log file analysis
parsing text into entities (ex. insurance forms)
traffic predictions
deeper user engagement insights
website audit insights
automatic website fixes
instant alerts on website errors + SERP flux

50. Problem: Customer Churn
To do this, you need to know:
• What Machine Learning is
• How to identify when you can use ML to solve problems
• Specific examples of ML in action
• How to integrate ML into your thinking process [Framework]
Solution: What sort of data + model might you need to do this?

51. Problem: Customer Churn
To do this, you need to know:
1. What Machine Learning is
2. How to identify ML opportunities
3. Specific examples of ML in action
4. How to integrate ML into your thinking process [Framework]
Solution: What sort of data + model might you need to do this?

52. https://towardsdatascience.com/hands-on-predict-customer-churn-5c2a42806266

53. 1. What would you like to solve for?
2. Do you have labeled data to help train a model?
3. If not, can you start to collect data to help solve for your problem?
4. If not, consider what data you currently have and what you could solve with it.
Simple ML Framework

54. bit.ly/ml-framework

55. Let’s solve a problem together using ML
How can we help solve a customer
churn problem?

56. Let’s solve a problem together using ML
What would we want a model to do
to prevent churn?

57. Classification: Spam / Not Spam
Association: If milk is in someone’s cart, they are
80% more likely to buy bread.
Regression: Prediction – Home price based on the
number of bedrooms, bathrooms, m2 ,etc.

58. Let’s solve a problem together using ML
What kind of data would we need
to train a model to do that?

64. Download GSC data
Get low CTR pages
Scrape page titles
Find top keywords per page
Find pages missing top keywords in their title
Rewrite/add keyword to the title

67. Getting Started
• Search ‘Harvard CS109’ in GitHub
• Learn Python in 10 Mins
• Google CodeLabs – Break things!!!
• MNist --The “Hello World!” of Machine Learning
• Colab Notebooks OR Jupyter Notebooks
• Learn With Google AI
• Image-net.org
• Kaggle
• MonkeyLearn

68. Top ML Books

69. Free ML Books: bit.ly/free-ml-books
• Statistics: New Foundations, Toolbox, and Machine Learning Recipes
• Classification and Regression in a Weekend
• Online Encyclopedia of Statistical Science
• Azure Machine Learning in a Weekend
• Enterprise AI - An Application Perspective
• Applied Stochastic Processes
(With a free Data Science Central account)

71. • 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
• Algorithmia
• 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

72. People to follow

73. ML for SEOs Takeaways:
• ML is programming + statistics that gives computers the ability to learn
• An ML model is only as good as its training data
• ML opportunities occur where available data can be used to predict, classify, discover associations/insights,
etc.
• Consider the data you have & what you could do with it
• Diversity is paramount in ML
• YOU can create an ML model today!!!

74. The Data Science Team at Moz is innovating in this space to make
your journey from data to insights more efficient

75. think differently

76. Thank You!

78. Wut?

79. Sentiment Analysis
Named entity recognition
Question and answering
Classification
Machine translation
Summarization
Sentence disambiguation

80. BERT combines and outperforms
10+ of the common NLP tools

81. BERT combines and outperforms
10+ of the common NLP tools
A pre-trained BERT model can be finetuned
with just one additional output layer to create a
SOTA model for wide range tasks such as
question answering.
Sound familiar??

83. +
+
+
+

85. What BERT can’t do

86. You can play around with BERT
today: