Posts about machine-learning

Word vectors are useful in NLP tasks to preserve the context or meaning of text data. In this post we will use Spacy to obtain word vectors, and transform the vectors into a feature matrix that can be used in a Scikit-learn pipeline.

Two broad categories in machine learning are supervised and unsupervised learning. Classification and clustering are examples of each of those respectively, and in this post I will go over the differences between them and when you might use them.

Feature Engineering is an important skill in data science, and is the process of taking raw data and turning it into features that can be used as inputs for training machine learning algorithms. We will look at 311 noise complaints data in this post.

Text classification is a popular and important problem that we deal with on a daily basis. I will be creating a text classifier with Python and scikit-learn to filter a collection of articles based on whether or not they are fashion-related or not.