Let's explore the fundamentals of Natural Language Processing (NLP) as you delve into text preprocessing techniques and various text models used to represent text data. You will gain practical insights and hands-on experience with the tools and methods essential for analyzing and interpreting textual data effectively. This course equips you with the skills to transform raw text into meaningful information, paving the way for advanced applications in AI and machine learning.

We will start our journey with learning and implementing the most common text preprocessing techniques used in NLP to convert the initial raw text into a clean, standardized form.

Without further ado, let's explore stemming and lemmatization. These techniques may enhance efficiency and effectiveness of some NLP tasks, especially when working with large text corpora and treating different words forms as the same word. 

Preprocessed text should then be transformed into a numerical representation to be used in machine learning or deep learning models for various tasks such as prediction, classification, or clustering. Here, we will learn to implement the most basic yet popular text models that transform text data into numbers.

Time to unlock the power of word embeddings and delve into advanced techniques for capturing semantic relationships between words. We will explore various embedding models such as Word2Vec, GloVe, and FastText, with a particular focus on the Word2Vec model and its implementation.

Introduction to NLP

Challenge: Tokenizing a Sentence

Solution

Challenge: Tokenizing a Sentence

Solution