This post shows how to load, use, and make your own word embeddings using Python. Use the Gensim and Spacy libraries to load pre-trained word vector models from Google and Facebook, or train custom models using your own data and the Word2Vec algorithm. This post is a direct follow-on from the introductory Word Embeddings post, and will show you how to get started using word vectors with your own models and systems.
This post provides an introduction to “word embeddings” or “word vectors”. Word embeddings are real-number vectors that represent words from a vocabulary, and have broad applications in the area of natural language processing (NLP). We examine training, use, and properties of word embeddings models, and look at how and why you should look to use word embeddings over older bag-of-words techniques in your data science and language modelling tasks.
Merging and Joining data sets are key activities of any data scientist or analyst. In this tutorial, we explore the process of combining datasets based on common columns quickly and easily with the Python Pandas library and it’s fast merge() functionality. Finally conquer merging and become a master with this 2-part tutorial.
Geocode your addresses for free with Python and Google For a recent project, I ported the “batch geocoding in R” script over to Python. The script allows geocoding of large numbers of string addresses to latitude and longitude values using the Google Maps Geocoding API. The Google Geocoding API is one of the most accurate geocoding …