Machine Learning with Python: NLP
In this campaign, we explore natural language processing (NLP), its applications, text preprocessing steps, various vectorization techniques, and use scikit-learn and nltk to solve NLP problems using classification
📄️ NLP Basics for Text Processing
The quest covers the fundamentals of text processing in NLP, including tokenization, stop words removal, stemming, and lemmatization using NLTK library in Python
📄️ Vectorization
In this quest, we learn about vectorization in NLP and cover three methods: Bag-of-Words (BoW), Term frequency-Inverse Document Frequency (TFIDF), and Word Embeddings. By the end, we will be able to perform vectorization and understand the differences between the techniques
📄️ Practicing NLP Techniques: Fake News Classifier
In this quest, we analyze a dataset of news articles to reinforce NLP techniques, preprocess the text, apply vectorization, and create a classification model for identifying real or fake news