Word2Vec: Understanding and Implementing Word Embeddings
├── Introduction
│   └── Defining Word2Vec
├── Setting Up the Environment
│   ├── Necessary Libraries
│   └── Sample Data Preparation
├── Word2Vec Model
│   ├── Data Preprocessing
│   ├── Model Training
│   └── Analyzing Word Embeddings
├── Practical Examples
│   └── Demonstrating Word Relationships
└── Conclusion
    └── Reflecting on Word2Vec Applications

1. Introduction

Defining Word2Vec

• An exploration into Word2Vec, a technique in NLP for converting words into numerical vector space, capturing semantic and contextual relationships.

2. Setting Up the Environment

Necessary Libraries

import gensim
from gensim.models import Word2Vec
import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize

Sample Data Preparation

• Preparing textual data for demonstrating Word2Vec's capabilities.

3. Word2Vec Model

Data Preprocessing

• Converting sentences into tokens, a fundamental step in NLP, to feed into the Word2Vec model.

# Sample sentence
sentence = "Word2Vec transforms words into a rich vector space."
tokens = word_tokenize(sentence.lower())

Model Training

• Training the Word2Vec model on the processed data.

# Training Word2Vec
word2vec_model = Word2Vec([tokens], vector_size=100, window=5, min_count=1, workers=4)

Analyzing Word Embeddings

• Understanding how words are represented as vectors and the relationship between them.