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You will learn to:
Scrape data using Python.
Preprocess text data.
Create a language detection model without complex computations.
Create a simple Flask application.
Skills
Natural Language Processing
Text Preprocessing
Data Cleaning
Prerequisites
Intermediate knowledge of Python
Understanding of machine learning
Familiarity with text preprocessing
Basic understanding of NLP concepts and techniques
Technologies
Flask
Python
Project Description
This project aims to develop a language detection system capable of identifying the language of a given text document. The system utilizes n-grams, sequences of contiguous items (typically characters or words), to extract language-specific patterns from the text. It involves several stages: data collection from public domain books in various languages, text tokenization, n-gram generation, and language identification based on comparing n-grams frequencies with pretrained language models.
Technologies and libraries employed include Python libraries for text processing and web scraping. The end product is a language detection system capable of identifying the language of input text. The application’s modularity allows for easy expansion with additional languages and, hence, a better language identification system.
Project Tasks
1
Introduction
Task 0: Get Started
Task 1: Import Libraries
2
Downloading and Preprocessing Data
Task 2: Get the Data
Task 3: Preprocess the Data
3
Frequency Profiling
Task 4: Generate N-Grams
Task 5: Count and Sort N-Grams by Frequency
Task 6: Call N-Grams Functions
4
Language Detection
Task 7: Preprocess the Test File
Task 8: Test the Model
5
Language Detection Application
Task 9: Create Frontend of the Application
Task 10: Handle and Route the Request Object
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.