Natural Language Processing Virtual Internship

In this task, students will learn how to preprocess text data, including tokenization, stopword removal, and stemming/lemmatization. They will also explore techniques for analyzing text data, such as word frequency analysis and sentiment scoring.

In this task, students will build a text classification model using logistic regression, a fundamental machine learning algorithm. They will learn how to prepare the data, train the model, and evaluate its performance.

In this task, students will develop a sentiment analysis model using a recurrent neural network (RNN), specifically a Long Short-Term Memory (LSTM) network. They will learn how to prepare the data, design the model architecture, and train the model to classify text data into positive, negative, or neutral sentiment.

In this task, students will explore the use of transformer-based models, such as BERT, for sentiment analysis. They will learn how to fine-tune a pre-trained transformer model for the sentiment analysis task and compare its performance to the RNN-based model developed in the previous task.

In this task, students will build a GAN-based text generation model. They will learn how to design the generator and discriminator networks, train the GAN, and generate new text samples.

In this task, students will extend the GAN-based text generation model to support conditional text generation, where the generated text is conditioned on some additional input (e.g., topic, sentiment, or style).

In this task, students will learn how to package their NLP model for deployment, integrate it into a web application or API, and test the deployment process.

In this task, students will learn how to optimize and maintain their NLP models over time, ensuring they continue to perform well in production.