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.

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Pandas – Python Data Analysis Library I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well. One aspect that I’ve recently been exploring is the task of […]

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“Is there any online reading or courses I can do to get into data analysis?” At my workplace, I get asked the question above. The question is usually posed by people typically with a finance background, who’s working as a management consultant. In this post I propose a learning path for such people to “get […]

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