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Use free Machine Learning APIs #brightonseo
The document discusses the use of machine learning for predictive analytics, particularly in pricing predictions based on historical data, and emphasizes the importance of building useful rather than perfect models. It highlights various applications of machine learning including sentiment analysis, content classification, and the development of recommendation engines using pre-trained models. Resources for learning machine learning and practical applications in SEO and data scraping are also provided.
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Use free Machine Learning APIs #brightonseo
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- 13. International Freelance SEO Brand Ambassador Majestic Cycling& Skating Science: Physics in particular http://www.cyclingacrosstheworld.com
- 14. “A computer programis 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
- 15. E: 50 yearsof 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
- 16. The goal ofML 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”
- 17. Document Sentiment analysisof 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" } ] }
- 19. You know what youare looking for What do these datapoints have in common?
- 20. E: 50 yearsof 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
- 21. No rules teached.It took Google’s AI thousands of games to detect losing was probably bad
- 23.
- 24. Best to startwith: • 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
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- 27. Mainly use pretrained models: – Spam classification of user generated content (comments & reviews) – Content classification – Text extraction from pages – Data gathering
- 28. • Query classification •Recommendation engines: internal linking based on both e-commerce, user behaviour and SEO metrics.
- 29.
- 32. • No NLPor Machine Learning knowledge is required. • Lot’s of pre trained models & you can train your own models
- 39. Machine Learning basedscraping,Yeah!
- 41.
- 43. 1. Collected allhotel reviews 2. Check sentiment and main entities 3. Upload search volume and e-commerce data per hotel 4. Update UX & internal linking accordingly
- 44.
- 45. 1. Collected allhotel reviews 2. Plotted against time 3. Extract upcoming entities and sentiments 4. Predict future search behaviour 5. Create landingpages for future targeting
- 47. How about usingMachine Learning
- 49. Tip: Check boththe homepage and the specific link page!
- 53. Input: a URL-> output: plain text
- 55.
- 59. • A listof links containing – Content language – Content topic – Spam probability – Content sentiment (if wanted) – Prioritized on language relevancy
- 60. • 10.000+ keywords?Use a ML classifier • Check for entities like places for local • Buying intent vs informational
- 61. Persona Customer journey stage Page Type Local identifier TagKeyword 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
- 64. "I liked thebook you gave me yesterday, but the rest of my day was terrible."
- 70. • Restructure websitecontent based on a set taxonomy of topics • Extract texts from top 30 and define text requirements (eg. Searchmetrics module) • Purchase prediction for new queries
- 74. • Use GoogleTensorflow 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
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