with Python and Dedupe
Entity resolution (ER) is the task of disambiguating records that correspond to real world entities across and within datasets. The applications of entity resolution are tremendous, particularly for public sector and federal datasets related to health, transportation, finance, law enforcement, and antiterrorism.
Unfortunately, the problems . . .
Preparing Your Data to be Explored
This is the second post in our Data Exploration with Python series. Before reading this post, make sure to check out Data Exploration with Python, Part 1!
Mise en place (noun): In a professional kitchen, the disciplined organization and preparation of equipment and food before service begins.
When performing exploratory data analysis (EDA), . . .
Part 1: Skip-gram Feedforward
Editor's Note: This post is part of a series based on the research conducted in District Data Labs' NLP Research Lab. Make sure to check out the other posts in the series so far:
- NLP Research Lab Part 1: Distributed Representations
- NLP Research Lab Part 2: Skip-Gram Architecture Overview
Let's continue our treatment of the . . .
Preparing Yourself to Become a Great Explorer
Exploratory data analysis (EDA) is an important pillar of data science, a critical step required to complete every project regardless of the domain or the type of data you are working with. It is exploratory analysis that gives us a sense of what additional work should be performed to quantify and extract insights from our data. It also . . .
Exceptions are a crucial part of higher level languages, and although exceptions might be frustrating when they occur, they are your friend. The alternative to an exception is a panic — an error in execution that at best simply makes the program die and at worst can cause a blue screen of death. Exceptions, on the other hand, are tools . . .
Posted in: python
An Overview and Tutorial
The amount of data generated each day from sources such as scientific experiments, cell phones, and smartwatches has been growing exponentially over the last several years. Not only are the number data sources increasing, but the data itself is also growing richer as the number of features in the data increases. Datasets with a large number . . .
Editor's Note: This post is part of a series based on the research conducted in District Data Labs' NLP Research Lab. Make sure to check out NLP Research Lab Part 1: Distributed Representations.
Chances are, if you’ve been working in Natural Language Processing (NLP) or machine learning, you’ve heard of the class of approaches called . . .