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