In machine learning, an ensemble model is one that combines multiple individual models such that it is built in order to perform better than each of its composing parts. The most famous ensemble method is probably Random Forest, which is an agglomerate of randomly created Decision Trees. A Random Forest prediction is then the average of the predictions made by its Decision Trees.
Having said that, much has been talked about black box machine learning models. That is, models that might (seem to) work well but that give its creators a hard time trying to explain their inner workings with precision. …
This past weekend I gave a talk at Python Conference Espirito Santo 2017. As it was a Python event I decided to talk about the Python tools I often use on my projects.
Python has 75% more Data Science and Machine Learning job openings than R on Indeed.com.
You can find the raw data I’ve used to calculate that here. As Python solidifies its position as the best programming tool to tackle Data Science and Machine Learning it comes as no surprise that half the talks at the conference were about these topics.
We started getting our hands dirty by taking a look at Jupyter Notebook, a pretty popular web app for mixing and matching code along with text and plots that I use in most of my projects. Here is an excerpt of the notebook I walked through during the…
This week I gave a talk entitled “Intro to Machine Learning for Hackers” at the Dev-ES meetup.
Machine Learning is a super cool subject with applications varying from speech recognition and recognizing objects in pictures to building self-driving cars. Deep Learning, which is a subfield of Machine Learning, is playing a crucial role in the business model of tech companies like Google, Facebook and Microsoft.
My main goal was to present an approach to studying Machine Learning that is mainly hands-on and abstracts most of the math for the beginner.
Many people get discouraged from books and courses that tell them as soon as they can that multivariate calculus, inferential statistics and linear algebra are prerequisites. Even though these things are (very) important, we might be able to get more Machine Learning Engineers out there by engaging them early with high-level code and only later presenting them with what is going on under the hood. …