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Use free Machine Learning APIs #brightonseo

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Machine learning sounds like something out of reach of the average marketer. Last year IBM opened up their super computer Watson, Microsoft launced Azure and also Amazon opened up their Machine Learning models to be commercially used. During this session I’ll introduce the concept of machine learning and share a few practical examples on how you can use it to optimize your SEO processes.

Published in: Internet

Use free Machine Learning APIs #brightonseo

  1. 1. International Freelance SEO
  2. 2.
  3. 3. International Freelance SEO Brand Ambassador Majestic Cycling & Skating Science: Physics in particular http://www.cyclingacrosstheworld.com
  4. 4. “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” -Tom Mitchell, Carnegie Mellon University
  5. 5. E: 50 years of data about housing prices in Brighton T: Pricing prediction to sell at right price P: the better price predictions it gives, the better future predictions will be
  6. 6. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. British mathematician and professor of statistics George E. P. Box that “all models are wrong, but some are useful”
  7. 7. Document Sentiment analysis of a specific URL: { "status": "OK", "url": " https://www.notprovided.eu/why-not-use-googles-wmt-data/ ", "totalTransactions": "1", "language": "english", "docSentiment": [ { "mixed": "1", "score": "0.412838", "type": "positive" } ] }
  8. 8. You know what you are looking for What do these datapoints have in common?
  9. 9. E: 50 years of data about housing prices in Brighton T: Pricing prediction to sell at right price P: the better price predictions it gives, the better future predictions will be
  10. 10. No rules teached. It took Google’s AI thousands of games to detect losing was probably bad
  11. 11. https://www.udacity.com/course/viewer#!/c-ud120/l-2254358555/m-2374468553
  12. 12. Best to start with: • https://www.coursera.org/learn/machine-learning by Andrew Ng (Baidu, former Google Brain) • Tom Mitchell lectures: http://www.cs.cmu.edu/~tom/10601_fall2012/lect ures.shtml • https://work.caltech.edu/telecourse.html Caltech ML course
  13. 13. http://pdf.th7.cn/down/files/1312/machine_learning_for_hackers.pdf
  14. 14. Mainly use pre trained models: – Spam classification of user generated content (comments & reviews) – Content classification – Text extraction from pages – Data gathering
  15. 15. • Query classification • Recommendation engines: internal linking based on both e-commerce, user behaviour and SEO metrics.
  16. 16. http://blog.mashape.com/list-of-50- machine-learning-apis/
  17. 17. • No NLP or Machine Learning knowledge is required. • Lot’s of pre trained models & you can train your own models
  18. 18. Machine Learning based scraping,Yeah!
  19. 19. https://www.notprovided.eu/7-tools-web-scraping-use- data-journalism-creating-insightful-content/
  20. 20. 1. Collected all hotel reviews 2. Check sentiment and main entities 3. Upload search volume and e-commerce data per hotel 4. Update UX & internal linking accordingly
  21. 21. ?
  22. 22. 1. Collected all hotel reviews 2. Plotted against time 3. Extract upcoming entities and sentiments 4. Predict future search behaviour 5. Create landingpages for future targeting
  23. 23. How about using Machine Learning
  24. 24. Tip: Check both the homepage and the specific link page!
  25. 25. Input: a URL -> output: plain text
  26. 26. Input text without HTML!
  27. 27. • A list of links containing – Content language – Content topic – Spam probability – Content sentiment (if wanted) – Prioritized on language relevancy
  28. 28. • 10.000+ keywords? Use a ML classifier • Check for entities like places for local • Buying intent vs informational
  29. 29. Persona Customer journey stage Page Type Local identifier Tag Keyword Leisure NL Awareness Product Yes Campingaz Campingaz Munich Leisure NL Awareness Informational No terrasverwarmer Leisure NL Awareness Informational No terrasverwarming Leisure NL Awareness Informational No BBQ gasbarbecue Leisure NL Awareness Informational No BBQ gas bbq Leisure NL Consideration Informational No Generic gasfles Leisure NL Retention Informational No Generic gasfles vullen Leisure NL Retention Informational No Branded primagaz Leisure NL Consideration Informational No Generic gasfles kopen B2B-industrie Awareness Informational No LNG lng Leisure NL Consideration Product No Generic gasflessen Leisure NL Awareness Informational No Generic kookplaat gas Energie Awareness Informational No Propaan propaan Leisure NL Awareness Informational No Butaan butaan
  30. 30. "I liked the book you gave me yesterday, but the rest of my day was terrible."
  31. 31. • Restructure website content based on a set taxonomy of topics • Extract texts from top 30 and define text requirements (eg. Searchmetrics module) • Purchase prediction for new queries
  32. 32. • Use Google Tensorflow to identify image contents • Crawl topic related content • Generate automatic descriptions and paragraph text • Build a image library site including text, good for SEO  https://databricks.com/blog/2016/01/25/deep-learning-with-spark-and-tensorflow.html
  33. 33. https://www.quora.com/Machine-Learning/How- do-I-learn-machine-learning-1
  34. 34.

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