Blog posts

Deep learning sounds so cool, doesn’t it? There is machine learning, which is already super cool and now make it deep. Even better. But what makes a learning algorithm deep?

In this episode, I talk to AWS data scientist Naz Levent. Naz has been with Amazon for more than two years and has been part of some exciting projects in fashion, energy and entertainment industries. She shares with us how she got into data science, how she got her first job, what her days look like and why she loves her job. She has some unusual and great advice on how to get ready for a data science career.

One of my student’s on the Hands-on Data Science course asked me an intriguing question this week. After completing the course, he reminded me of the project goal we formulated at the beginning of the course and wanted to know how the model we built helped solved this problem that we defined. And the answer to his questions was: it doesn't. But with good reason.

Have you heard about the recent groundbreaking achievements in NLP? Wow, you have to read this paper... Everyone is talking about this woman/man in AI. If reading headlines like these stress you out, you're not alone.

Khuyen Tran is a student and a data scientist who got her first job before even graduating. She tells me all about how she got her first job as a data scientist while still studying, how her writing on Medium brought a lot of opportunities to her foot, what resources she used to gain new skills and more. Do not miss this episode!

By now you have probably heard this explanation: ML algorithms learn like humans. You give it examples and it recognizes and remembers the patterns in them. You need to give it a lot of examples, though, so that it can learn accurately. Okay, that’s clear. But HOW does it learn?

Learning Python and Machine Learning are two of the most fundamental things you need to do to become a data scientist. Out of the two, learning Python is relatively easy. Learning machine learning though is when things get very overwhelming very fast. There are many concepts you need to be aware of and they have very intertwined relationships. That's why you need to have a smart strategy when learning them. I talk about my own approach in this article.

In this episode, I talk to Sadie St. Lawrence, the founder of Women in Data. Women in Data is an international organization that aims to increase diversity in data careers by providing awareness and education to its members. Listen to my chat with Sadie to find out how she herself became a data scientist, what advice she has for the aspiring data scientist, how you can also become part of Women in Data and much more!

Two weeks ago, a bunch of people stormed the Capitol building in the U.S. This whole situation reminded me of a saying we have in computer/data science: garbage in, garbage out. In this article, I explain why.