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Machine Learning - Know Enough To Be Dangerous #SearchLove


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SearchLove talk on Machine Learning for SEOs. Pick up resources, tips and tricks to get started with ML today!

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Machine Learning - Know Enough To Be Dangerous #SearchLove

  1. 1. Machine Learning: Know Enough To Be Dangerous Britney Muller Senior Data Scientist @ Moz
  2. 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: –
  3. 3.
  4. 4. #1 MACHINE LEARNING TIP: Find models that already do the thing you are trying to do and feed it YOUR data!
  5. 5. What if…
  6. 6. After 30 Epochs
  7. 7. After 100 Epochs
  8. 8. After 200 Epochs
  9. 9. ML is everywhere!
  10. 10. 1. Let’s break down Machine Learning 2. How to leverage ML to be a dangerous SEO 3. Tools & Resources
  11. 11. 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. 12. Supervised vs. Unsupervised
  13. 13. Three Common Models:
  14. 14. But, how do ML models get smarter?
  15. 15. The Loss Function:
  16. 16. Overfitting is a common problem:
  17. 17. 10 Year Challenge
  18. 18.
  19. 19. 1. Let’s break down Machine Learning 2. How to leverage ML to be a dangerous SEO 3. Tools & Resources
  20. 20. Writing Meta Descriptions
  21. 21. New Auto Generated Previous Google Generated
  22. 22. @jroakes @GraysonParks Grayson Parks Writer, programmer, constant learner. Digital marketer, husband, golden retriever owner. Words and data are my passions. #SEO @AdaptPartners JR Oakes Love working with smart people on smart things. Tech SEO Director at @adaptpartners
  23. 23. 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): lambda-functions/ 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: Find a developer familiar with AWS to:
  24. 24. function pageDescription(url, length) { if (typeof length == 'undefined' || !length || length < 1){ var endpoint = '' + url; }else{ var endpoint = '' + 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)
  25. 25. Use Text Summarization Algorithms to Help Aid the Writing of Meta Descriptions (GitHub Repo)
  26. 26. Automated Image Optimization
  27. 27. Podcasts
  28. 28. The average podcast listener consumes 7 different podcasts a week. -
  29. 29. JSON Output example (jq to parse)
  30. 30. 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:
  31. 31. 1. Let’s break down Machine Learning 2. How to leverage ML to be a dangerous SEO 3. Tools & Resources
  32. 32. CPU > GPU > FPGAs > TPU Flexibility< ------------------------------------------- > Power
  33. 33. Machine Learning Toolkit For SEOs Search ‘Harvard CS109’ in GitHub Google CodeLabs – Break things!!! Colab Notebooks OR Jupyter Notebooks Learn With Google AI (don’t forget it’s racist tho!) Kaggle AI For Everyone (Coursera) Seedbank PEAR – People + AI Research Tensorflow.js Getting Started Resources
  34. 34. 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
  35. 35. @BritneyMuller Thank You!