Posts about machine-learning

using word vector features with scikit-learn (featuring spacy)
Building a custom Scikit-learn Transformer using GloVe vectors from Spacy as features

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.

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Classification vs. Clustering
Classification vs. Clustering in Machine Learning

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.

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Feature Engineering with Python + Pandas: An Introduction

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.

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Classifying Fashion Articles with Python and Scikit-learn

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.

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