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Machine Learning For SEOs - TechSEOBoost 2018

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ATTN: Digital Marketers!!! Why you should care about Machine Learning:
1. It already affects the work that you're doing.
2. You should be able to speak intelligently about it.
3. It powers up your arsenal 10 fold.

Published in: Education

Machine Learning For SEOs - TechSEOBoost 2018

  1. 1. Britney Muller | @BritneyMuller | #TechSEOBoost Machine Learning For SEOs – Predict, Automate & Transcribe
  2. 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: –
  3. 3. Britney Muller | @BritneyMuller | #TechSEOBoost
  4. 4. Britney Muller | @BritneyMuller | #TechSEOBoost
  5. 5. Britney Muller | @BritneyMuller | #TechSEOBoost bit.ly/tf-for-poets
  6. 6. Britney Muller | @BritneyMuller | #TechSEOBoost
  7. 7. Britney Muller | @BritneyMuller | #TechSEOBoost
  8. 8. Britney Muller | @BritneyMuller | #TechSEOBoost
  9. 9. Britney Muller | @BritneyMuller | #TechSEOBoost
  10. 10. Britney Muller | @BritneyMuller | #TechSEOBoost
  11. 11. Britney Muller | @BritneyMuller | #TechSEOBoost
  12. 12. Britney Muller | @BritneyMuller | #TechSEOBoost
  13. 13. Britney Muller | @BritneyMuller | #TechSEOBoost 65% Probability this is Rand!
  14. 14. Britney Muller | @BritneyMuller | #TechSEOBoost Automated Image Optimization
  15. 15. Britney Muller | @BritneyMuller | #TechSEOBoost ML is everywhere!
  16. 16. Britney Muller | @BritneyMuller | #TechSEOBoost Smart Compose
  17. 17. Britney Muller | @BritneyMuller | #TechSEOBoost 1.Let’s break down Machine Learning 1.How can you apply ML to SEO 1.Tools & Resources
  18. 18. 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.
  19. 19. Britney Muller | @BritneyMuller | #TechSEOBoost Supervised vs. Unsupervised
  20. 20. Britney Muller | @BritneyMuller | #TechSEOBoost
  21. 21. Britney Muller | @BritneyMuller | #TechSEOBoost Three Common Models: Home size vs selling price Duck or Snake?Animal Types
  22. 22. Britney Muller | @BritneyMuller | #TechSEOBoost
  23. 23. Britney Muller | @BritneyMuller | #TechSEOBoost But, how do ML models get smarter?
  24. 24. Britney Muller | @BritneyMuller | #TechSEOBoost The Loss Function:
  25. 25. Britney Muller | @BritneyMuller | #TechSEOBoost Overfitting is a common problem:
  26. 26. Britney Muller | @BritneyMuller | #TechSEOBoost
  27. 27. Britney Muller | @BritneyMuller | #TechSEOBoost If Machine Learning was a car, data would be the fuel
  28. 28. Britney Muller | @BritneyMuller | #TechSEOBoost
  29. 29. Britney Muller | @BritneyMuller | #TechSEOBoost
  30. 30. Britney Muller | @BritneyMuller | #TechSEOBoost 1.Let’s break down Machine Learning 1.How can you apply ML to SEO 1.Tools & Resources
  31. 31. Britney Muller | @BritneyMuller | #TechSEOBoost
  32. 32. Britney Muller | @BritneyMuller | #TechSEOBoost
  33. 33. Britney Muller | @BritneyMuller | #TechSEOBoost Writing Meta Descriptions Sucks
  34. 34. Britney Muller | @BritneyMuller | #TechSEOBoost
  35. 35. Britney Muller | @BritneyMuller | #TechSEOBoost
  36. 36. Britney Muller | @BritneyMuller | #TechSEOBoost
  37. 37. Britney Muller | @BritneyMuller | #TechSEOBoost
  38. 38. Britney Muller | @BritneyMuller | #TechSEOBoost Can you believe?! Auto Generated Google Generated
  39. 39. 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
  40. 40. Britney Muller | @BritneyMuller | #TechSEOBoost
  41. 41. 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:
  42. 42. 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)
  43. 43. Britney Muller | @BritneyMuller | #TechSEOBoost
  44. 44. Britney Muller | @BritneyMuller | #TechSEOBoost Use Text Summarization Algorithms to Help Aid the Writing of Meta Descriptions (GitHub Repo)
  45. 45. Britney Muller | @BritneyMuller | #TechSEOBoost Podcasts
  46. 46. Britney Muller | @BritneyMuller | #TechSEOBoost The average podcast listener consumes 7 different podcasts a week. -https://www.podcastinsights.com/podcast-statistics/
  47. 47. Britney Muller | @BritneyMuller | #TechSEOBoost
  48. 48. Britney Muller | @BritneyMuller | #TechSEOBoost
  49. 49. Britney Muller | @BritneyMuller | #TechSEOBoost JSON Output example (jq to parse)
  50. 50. 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:
  51. 51. Britney Muller | @BritneyMuller | #TechSEOBoost 1.Let’s break down Machine Learning 1.How can you apply ML to SEO 1.Tools & Resources
  52. 52. 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:
  53. 53. Britney Muller | @BritneyMuller | #TechSEOBoost
  54. 54. Britney Muller | @BritneyMuller | #TechSEOBoost
  55. 55. Britney Muller | @BritneyMuller | #TechSEOBoost
  56. 56. Britney Muller | @BritneyMuller | #TechSEOBoost
  57. 57. Britney Muller | @BritneyMuller | #TechSEOBoost
  58. 58. Britney Muller | @BritneyMuller | #TechSEOBoost
  59. 59. Britney Muller | @BritneyMuller | #TechSEOBoost CPU > GPU > TPU
  60. 60. 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
  61. 61. 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
  62. 62. Britney Muller | @BritneyMuller | #TechSEOBoost What did we learn?
  63. 63. 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
  64. 64. Britney Muller | @BritneyMuller | #TechSEOBoost What did we learn? Thank you! @BritneyMuller britney@moz.com

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