This repository contains examples of machine learning tasks implemented in Python using various libraries such as NLTK, BeautifulSoup, NumPy, Pandas, and more.
Machine learning (ML) is an umbrella term for solving problems for which the development of algorithms by human programmers would be cost-prohibitive. Instead, these problems are solved by helping machines 'discover' their 'own' algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches. ML approaches have been applied to various domains such as large language models, computer vision, speech recognition, email filtering, agriculture, and medicine.
- Web Scraping with BeautifulSoup
- Text Preprocessing with NLTK
- Word Cloud Generation
- Part-of-Speech (POS) Tagging
demonstrates how to scrape text from a webpage using BeautifulSoup.
showcases various text preprocessing techniques such as sentence tokenization, word tokenization, and stop word removal using NLTK.
generates a word cloud visualization from a given text using the WordCloud library.
to perform Part-of-Speech (POS) tagging on a given text using NLTK.
Check out the examples directory for sample text files and corresponding Python scripts demonstrating each task.
- Python 3.x
- NLTK
- BeautifulSoup
- NumPy
- Pandas
- Matplotlib
- WordCloud