## Understanding Stemming

To start off, let's first understand what stemming essentially is. 

To be more precise, **stemming** involves removing suffixes from words to obtain their root form, known as the **stem**. For example, the stems of "running," "ran," and "runner" are all "run." As mentioned above, the purpose of stemming is to simplify the analysis by treating similar words as the same entity, ultimately enhancing the efficiency and effectiveness of various NLP tasks.

## Stemming with NLTK

NLTK provides various stemming algorithms, with the most popular being the **Porter Stemmer** and the **Lancaster Stemmer**. These algorithms apply specific rules to strip affixes and derive the stem of a word.

All of the stemmer classes in NLTK share a common interface. First, you have to **create an instance** of the stemmer class and then use its `stem()` method for each of the tokens. Let's take a look at the following example:

import nltk
from nltk.stem import PorterStemmer, LancasterStemmer
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords

nltk.download('punkt')
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))

# Create a Porter Stemmer instance
porter_stemmer = PorterStemmer()
# Create a Lancaster Stemmer instance
lancaster_stemmer = LancasterStemmer()

text = "Stemming is an essential technique for natural language processing."
text = text.lower()
tokens = word_tokenize(text)

# Filter out the stop words
tokens = [token for token in tokens if token.lower() not in stop_words]

# Apply stemming to each token
porter_stemmed_tokens = [porter_stemmer.stem(token) for token in tokens]
lancaster_stemmed_tokens = [lancaster_stemmer.stem(token) for token in tokens]

# Display the results
print("Original Tokens:", tokens)
print("Stemmed Tokens (Porter Stemmer):", porter_stemmed_tokens)
print("Stemmed Tokens (Lancaster Stemmer):", lancaster_stemmed_tokens)

As you can see, there is nothing complicated here. First, we applied tokenization, then filtered out the stop words and finally applied stemming on our tokens using **list comprehension**. Speaking of the results, these two stemmers produced rather different results. This is due to the fact that the **Lancaster Stemmer** has about twice as many rules as the **Porter Stemmer** and is one of the most "aggressive" stemmers.

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

Stemming

Understanding Stemming

Stemming with NLTK