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Use SERP data to build machine learning models

The largest data samples produce the best models.

Google Images results

Extract and use images from our Google Images API to train your own machine learning models. Google Images is the best source of images you can find for machine learning data. It's diverse, universal, and already labeled.

  • Prepare the dataset for training a machine learning model by accessing hundreds of images, including their thumbnail, title, and URL.
  • Build your own neural network and solve the challenge of image classification by using our API.
Documentation:

Text results

Regular organic results are a rich and powerful source of data for your machine learning models. They are already structured and clear.

  • Extract the text behind each webpage.
  • Build a question-answering model on top of the collection of texts related to the query.
  • Use natural language to understanding the semantic data layer that Google uses to rank results.
Documentation:

Google Scholar results

Another rich source of data is our Google Scholar API. Get access to an engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and publishers.

  • Use scholarly data to build machine learning prediction models.
  • Mine scientific articles and map thousands of matching documents to get the best overview of what should be relevant to a certain research question.
Documentation:

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Grubhub
Ibm
Morgan Stanley
Experian
Kpmg
Pwc
Airbnb
Harvard University
Un
Thomson Reuters
Nyt
Bright Local
Kraken
Columbia University
The Hoth