Launchpad – programming environment setup

We will use the fast.ai machine learning setup for this exercise. Their setup environment is great for personal experimentation and industry-grade proof-of-concept projects. I have used the fast.ai environment on both Linux and Windows. We will use Python 3.6 here since our code will not run for other Python versions.

A quick search on their forums will also take you to the latest instructions on how to set up the same on most cloud computing solutions including AWS, Google Cloud Platform, and Paperspace. 

This environment covers the tools that we will use across most of the major tasks that we will perform: text processing (including cleaning), feature extraction, machine learning and deep learning models, model evaluation, and deployment.

It includes spaCy out of the box. spaCy is an open source tool that was made for an industry-grade NLP toolkit. If someone recommends that you use NLTK for a task, use spaCy instead. The demo ahead works out of the box in their environment.

There are a few more packages that we will need for later tasks. We will install and set them up as and when required. We don't want to bloat your installation with unnecessary packages that you might not even use.