Building a deep learning classifier using Python and TensorFlow — Part 1: The data
Since I started my machine learning journey not so long ago, I was immediately intrigued by the potential that Neural Networks had for solving complex problems and my curiosity led me to try to discover as many different applications as I could. Leaving no stone unturned, I quickly found that image recognition was perhaps the most exciting for a relative newcomer like myself and has arguably been the most widely used application of neural networks and artificial intelligence in modern society.
How to build a REST API using Python and Flask
If, like me, you find the prospect of building your own API exciting but have no idea where to start — look no further. This article serves as a gentle guide towards building your own REST API using Python and the Flask framework.
The purpose of an API is to share data from one application to another.
Before we jump in, let’s get to know the Flask library a little better —
Flask is a Python library for web application development that was created as a beginner friendly “micro” framework…
Just like regression, classification algorithms are the bread to our data science butter and all serious data science enthusiasts should have a few classification algorithms under their belt. In this article, I will be taking you through the exploration of the penguins dataset and we will ultimately be fitting a decision tree classifier to predict a penguin’s species.
In this example, we will be using Python’s pmlb library and the penguins dataset. If you don’t have the pmlb library installed, go ahead and do so.
pip install pmlb
Next, we import our plotting libraries that will aid in the exploration…