Well, I had to make the title catchy to get you to read this, right?
This is one of the introductory concepts to Machine Learning, and it’s crucial to understand the difference, as every problem has a distinct solution. Reading this will hopefully help you solve problems quicker!
This article is inspired by a lesson in Udacity’s Foundation course for Microsoft Azure Machine Learning. I was lucky to be selected as one of the recipients for the scholarship for the first phase, so I’m sharing some pointers that may be of help for beginners taking a stroll in the field…
Ever wondered how to deploy your machine learning model in the form of an app, without using ML Studio, Flask or Django? Well, there’s a pretty effective solution that goes by the name Streamlit.
It’s an open-source framework that allows you to create machine learning and data science apps for free. So if you’ve worked extensively on your deep learning project or some cool data visualizations, and want to showcase your intellect and efforts, here’s your chance.
Did I mention it’s absolutely free?
Student — Machine Learning Enthusiast