In this week’s Python Data Weekly Roundup:
If you haven’t taken a look at time-series databases, you should. For a lot of what we do today in data science, a time series database might make sense (e.g., stream processing,etc). While this article isn’t long, its a quick introduction to the topic.
This is a very good article describing some of the challenges that data scientist face today. Its not all rosy out there…there are a lot of challenges and issues. This is definitely worth a read…I wonder how many of you are seeing these in your current jobs?
What is BERT?
BERT (Bidirectional Encoder Representations from Transformers) is Google’s deep learning algorithm for NLP (natural language processing). It helps computers and machines understand the language as we humans do. Put simply, BERT may help Google better understand the meaning of words in search queries.
If you’ve ever had to work on customer churn, you know it can be tough. This article describes how to use categorical features to understand customer churn. Its really good and worth the effort to read / follow.
When working with data and modeling, its sometimes hard to determine what model you should use for a particular modeling project. A quick way to find an algorithm that might work better than others is to run through an algorithm comparison loop to see how various models work against your data. In this post, I compare machine learning methods using a few different sklearn algorithms.
What’s happening in artificial intelligence in the year ahead? Look for modeling at the edge, new attention to data governance, and continued talent wars, among key AI trends.
That’s it for this week’s Python Data Weekly Roundup. Subscribe to our newsletter to receive this weekly roundup in your email.