Data Science Projects |Part 2 | Language Identification using CNN

Welcome to Part 2 of our Data Science Project series!

In this session we embark on an exciting journey to build a language identification system using Convolutional Neural Networks (CNNs). Language identification is a crucial step in natural language processing (NLP) that helps in detecting the language of a given text.

What You'll Learn:

- Introduction to Language Identification: Understand the importance of identifying the language of a text in various applications such as text preprocessing, multilingual systems, and content filtering.

-Overview of Convolutional Neural Networks: Get introduced to CNNs and their application in text classification tasks, including the key concepts and architecture of CNNs.

-Dataset Preparation: Explore commonly used datasets for language identification and learn how to preprocess text data for training, including text normalization, tokenization, and vectorization.

-Building a CNN Model: Step-by-step guide on constructing a CNN model for language identification using a deep learning framework like TensorFlow or PyTorch.

- Training the Model: Learn how to train your CNN model on the preprocessed dataset, including setting up the training parameters, handling overfitting, and monitoring the training process.

-Model Evaluation: Introduction to evaluating the performance of your language identification model using metrics such as accuracy, precision, recall, and F1 score.

Why Watch This session?

This session is essential for data scientists and NLP enthusiasts who want to master language identification using CNNs. By the end of this session, you'll have a solid understanding of CNNs, data preprocessing techniques, and how to build and evaluate a language identification model.

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