NLP Hands On

Keyword Extractor Keyword Extractor A step-by-step guide to automatically identifying the most important keywords from a document using TF-IDF . Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: sklearn: Provides `TfidfVectorizer` for keyword scoring. nltk: Used for text preprocessing like stop word removal. 1. Data Loading and Preprocessing What the code does: It takes a single document and preprocesses it by converting it to lowercase and removing punctuation. Behind the scenes: S...

Speech-to-Text with a Simple Model Speech-to-Text with a Simple Model A step-by-step guide to converting spoken words into text using the SpeechRecognition library. Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: SpeechRecognition: The core library for performing speech recognition. It acts as an interface for various speech recognition engines. pydub: Used for handling and playing audio files. os: For handling file system operations. 1. Audio File Setup What the code does: It creates a temp...

Text-Based Recommendation System Text-Based Recommendation System A step-by-step guide to building a content-based recommendation system that suggests items based on text similarity. Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: sklearn: Provides `TfidfVectorizer` and `cosine_similarity` for the core recommendation logic. pandas: Used for data manipulation, particularly for creating and handling a DataFrame of items. 1. Data Loading and Preprocessing What the code does: It creates a small dataset of "articles" with titles and content...

Topic Modeling Topic Modeling A step-by-step guide to discovering underlying topics in a collection of documents using Latent Dirichlet Allocation (LDA) . Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: gensim: A library for topic modeling and vector space modeling. It provides the implementation of LDA. nltk: For text preprocessing tasks like stop word removal and lemmatization. re: For regular expression-based text cleaning. 1. Data Loading and Preprocessing What the code does: It takes a c...

Language Translation Language Translation A step-by-step guide to building a simple sequence-to-sequence (seq2seq) model for language translation. Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: tensorflow: The core deep learning framework for building the seq2seq model. numpy: Used for numerical operations. 1. Data Loading and Preprocessing What the code does: It uses a small, hand-crafted dataset of English-to-French phrases. It tokenizes the text, creates a vocabulary for both languages, and pads the sequences to a uniform length. ...

Named Entity Recognition (NER) Named Entity Recognition (NER) A step-by-step guide to developing a model that can identify and classify named entities in text. Libraries Used This pipeline uses a few key Python libraries. Here is a quick overview of their roles: nltk: The Natural Language Toolkit, used for tokenization and its built-in NER functionality. 1. Data Loading and Preprocessing What the code does: It takes a sample block of text and performs two crucial preprocessing steps: word tokenization (splitting the text into words) and part-of-speech (POS) tagging (labeling each word with its grammatical function). ...