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International Freelance SEO

International Freelance SEO
Brand Ambassador Majestic
Cycling & Skating
Science: Physics in particular
http://www.cyclingacrosstheworld.com/
The field of
“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
E: 50 years of data about housing prices in
Munich
T: Pricing prediction to sell at right price
P: the better price predictions it gives, the
better future predictions will be
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”
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"
}
]
}
You know
what you are
looking for
What do these
datapoints have
in common?
E: 50 years of data about housing prices
in Munich
T: Pricing prediction to sell at right price
P: the better price predictions it gives, the
better future predictions will be
No rules teached. It took Google’s AI thousands of games to detect losing was probably bad
http://www.slideshare.net/roelofp/deep-learning-as-a-catdog-detector
No Free Lunch Theorem
Never test your classifier on your input data.
Always keep at least 10% of available
training data for testing and evaluation
purposes
https://www.udacity.com/course/viewer#!/c-ud120/l-2254358555/m-2374468553
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
http://pdf.th7.cn/down/files/1312/machine_learning_for_hackers.pdf
Mainly use pre trained models:
– Spam classification of user generated content
(comments & reviews)
– Content classification
– Text extraction from pages
• Query classification
• Recommendation engines: internal linking
based on both e-commerce, user
behaviour and SEO metrics.
http://blog.mashape.com/list-of-50-
machine-learning-apis/
• No NLP or Machine Learning knowledge is
required.
• Lot’s of pre trained models & you can train
your own models
Machine Learning based scraping,Yeah!
https://www.notprovided.eu/7-tools-web-scraping-use-
data-journalism-creating-insightful-content/
1. Collected all hotel reviews
2. Check sentiment and main entities
3. Upload search volume and e-commerce
data per hotel
4. Update internal linking accordingly
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
How about using Machine Learning
Tip: Check both the homepage and the specific link page!
Input: a URL -> output: plain text
• A list of links containing
– Content language
– Content topic
– Spam probability
– Content sentiment (if wanted)
– Prioritized on language relevancy
• 10.000+ keywords? Use a ML classifier
• Check for entities like places for local
• Buying intent vs informational
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
"I liked the book you gave me yesterday, but
the rest of my day was terrible."
{ "summarized_data": “Mallorcan roads are well
maintained, cyclist are really welcome and I really
enjoyed it last year...", "auto_gen_ranked_keywords": [
"flight", "madrid", "mallorca", "training", "food", "plane",
"delayed", "weather", "broken", "quest", "hot", "spirit",
"horror", "booked", "hour", "wifi", "trip", "situation", "airport",
"gate", "mallorcan", "lounge", "spend", "minute", "ve",
"cyclist", "rainy", "missed", "netherland", "enjoyed", "road" ]
}
• Facial recognition after account creation
Aw! Yes, said Miss Skinlin she hasn’t the
first heir to the female figure. The waves
dance bright and happy when I forgot to
learn, before which she told me to read and
study. My Uncle, with a commanding, What
are you better than Kintuck.
19th century American literature
http://blog.algorithmia.com/2015/12/nanogenmo-text-analysis-with-algorithmias/
1. Input topic & Scrape current content
2. Create all N-grams
3. Create individual paragraphs
4. Randomly combine and create texts
5. Run through topic and sentiment classifiers to
evaluate
https://algorithmia.com/algorithms/lizmrush/GenerateParagraphFromTrigram
• 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
• 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
• From 2011: Google Prediction API
http://cloudacademy.com/blog/google-prediction-api/
https://www.quora.com/Machine-Learning/How-
do-I-learn-machine-learning-1
Using Free Machine Learning API's for SEO - #SMX Munich 2016
Using Free Machine Learning API's for SEO - #SMX Munich 2016

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Using Free Machine Learning API's for SEO - #SMX Munich 2016

  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15. International Freelance SEO Brand Ambassador Majestic Cycling & Skating Science: Physics in particular http://www.cyclingacrosstheworld.com/
  • 17. “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
  • 18. E: 50 years of data about housing prices in Munich T: Pricing prediction to sell at right price P: the better price predictions it gives, the better future predictions will be
  • 19. 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”
  • 20. 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" } ] }
  • 21.
  • 22. You know what you are looking for What do these datapoints have in common?
  • 23. E: 50 years of data about housing prices in Munich T: Pricing prediction to sell at right price P: the better price predictions it gives, the better future predictions will be
  • 24. No rules teached. It took Google’s AI thousands of games to detect losing was probably bad
  • 25.
  • 27. No Free Lunch Theorem
  • 28.
  • 29. Never test your classifier on your input data. Always keep at least 10% of available training data for testing and evaluation purposes
  • 31. 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
  • 33.
  • 34.
  • 35.
  • 36. Mainly use pre trained models: – Spam classification of user generated content (comments & reviews) – Content classification – Text extraction from pages
  • 37. • Query classification • Recommendation engines: internal linking based on both e-commerce, user behaviour and SEO metrics.
  • 39.
  • 40.
  • 41. • No NLP or Machine Learning knowledge is required. • Lot’s of pre trained models & you can train your own models
  • 42.
  • 43.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48. Machine Learning based scraping,Yeah!
  • 49.
  • 51.
  • 52. 1. Collected all hotel reviews 2. Check sentiment and main entities 3. Upload search volume and e-commerce data per hotel 4. Update internal linking accordingly
  • 53.
  • 54. 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
  • 55.
  • 56. How about using Machine Learning
  • 57.
  • 58.
  • 59.
  • 60. Tip: Check both the homepage and the specific link page!
  • 61.
  • 62.
  • 63.
  • 64. Input: a URL -> output: plain text
  • 65.
  • 66.
  • 67.
  • 68.
  • 69.
  • 70.
  • 71. • A list of links containing – Content language – Content topic – Spam probability – Content sentiment (if wanted) – Prioritized on language relevancy
  • 72. • 10.000+ keywords? Use a ML classifier • Check for entities like places for local • Buying intent vs informational
  • 73. 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
  • 74.
  • 75.
  • 76. "I liked the book you gave me yesterday, but the rest of my day was terrible."
  • 77.
  • 78.
  • 79.
  • 80. { "summarized_data": “Mallorcan roads are well maintained, cyclist are really welcome and I really enjoyed it last year...", "auto_gen_ranked_keywords": [ "flight", "madrid", "mallorca", "training", "food", "plane", "delayed", "weather", "broken", "quest", "hot", "spirit", "horror", "booked", "hour", "wifi", "trip", "situation", "airport", "gate", "mallorcan", "lounge", "spend", "minute", "ve", "cyclist", "rainy", "missed", "netherland", "enjoyed", "road" ] }
  • 81. • Facial recognition after account creation
  • 82.
  • 83. Aw! Yes, said Miss Skinlin she hasn’t the first heir to the female figure. The waves dance bright and happy when I forgot to learn, before which she told me to read and study. My Uncle, with a commanding, What are you better than Kintuck. 19th century American literature http://blog.algorithmia.com/2015/12/nanogenmo-text-analysis-with-algorithmias/
  • 84. 1. Input topic & Scrape current content 2. Create all N-grams 3. Create individual paragraphs 4. Randomly combine and create texts 5. Run through topic and sentiment classifiers to evaluate
  • 85.
  • 86.
  • 87.
  • 89.
  • 90.
  • 91. • 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
  • 92.
  • 93.
  • 94.
  • 95. • 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
  • 96. • From 2011: Google Prediction API http://cloudacademy.com/blog/google-prediction-api/