Appendix: NLP and ML Fundamentals

In this course, we will explore the fascinating field of NLP and how it has evolved over time. We will start by diving into the history of NLP and its applications, as well as the various challenges and techniques involved in processing natural language text.

We will then move on to the topic of text pre-processing, where we will discuss how to clean and prepare text data for analysis. This includes techniques such as tokenization, stemming, and lemmatization. After this, we'll cover some of the most important techniques used to turn text into lists of numbers (or vectors), for the models to be able to process them.

Next, we will focus on building and evaluating classifiers, which are algorithms that can automatically classify text into different categories or classes. We will cover techniques such as feature engineering, supervised learning, and evaluation metrics.

Are you ready? Let's start learning!