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Improving Data Quality for Superior Results
When you’re a data scientist, you see a problem, and you build a model to solve it. If it’s not as accurate as you were hoping, you tweak the model. But what if it’s the quality of your data causing skewed or flawed results? Kaitlin Andryauskas... Read more
Dr. David Armstrong Added to Open Data Science Conference Europe Keynote Lineup
It’s not often that humans discover a new planet, let alone 50 at once. And when we do discover one, it’s often through satellites, telescopes, and other traditional means. Recently, however, 50 planets were discovered via a machine learning algorithm largely written by Dr. David Armstrong... Read more
Could Your Machine Learning Model Survive the Crisis: Monitoring, Diagnosis, and Mitigation Part 1
As the world is changing rapidly around us, it is often questionable whether something we learned from the past is still valid. Machine learning models that make predictions of the future based on past data points are probably under most scrutiny from businesses in the current... Read more
Building a Production-Level Data Pipeline Using Kedro
How can Kedro help you? Suppose you are a self-taught data scientist who does not have much experience in software development. One morning, your senior executive asks you to provide an ad-hoc analysis – perks of the job, and when you do, she thanks you for... Read more
From Good to Great: The 5 Skills You Need to Shine in Data Science
Almost three years ago, I switched from a career in academia to a career in business in a data science role. This used to be somewhat of a rare event, but today it is commonplace: not only is there a shortage of data scientists, but also... Read more
How to Explain Your ML Models?
Explainability in machine learning (ML) and artificial intelligence (AI) is becoming increasingly important. With the increased demand for explanations and the number of new approaches out there, it could be difficult to know where to start. In this post, we will get hands-on experience in explaining... Read more
Intro to Vectors and Matrices in Machine Learning
Programming is a great way to get insights about math concepts. You’ll see here tips and tricks to learn math, more specifically linear algebra, from a coding perspective. You’ll see the relationship between Numpy functions and linear algebra abstract concepts. At the end of this mini-tutorial,... Read more
Autograd is PyTorch’s automatic differentiation package. Thanks to it, we don’t need to worry about partial derivatives, chain rule, or anything like it. To illustrate how it works, let’s say we’re trying to fit a simple linear regression with a single feature x, using Mean Squared... Read more
To Be or Not to Be an Anomaly?
An outlier may be defined as an object that is out of ordinary, which differs significantly from the norm. In day to day examples, it could be a baby panda among adult pandas, a champion breaking a world record, or fraud emails in your inbox. Why... Read more
Continuous Delivery for Machine Learning
Why is bringing machine learning code into production hard? Machine Learning applications are becoming popular in all industries. However, the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application. These... Read more