This repository includes various projects that explore Natural Language Processing (NLP) and Machine Learning techniques. These projects range from fundamental text processing techniques to advanced applications using deep learning and cloud deployment. Below is an overview of the key repositories and their respective contents.
This section contains fundamental text processing techniques used in NLP, including:
- Tokenization: Dividing text into smaller units such as words or subwords.
- Stemming: Reducing words to their root form (e.g., "running" → "run").
- Lemmatization: Converting words to their base form considering context.
- Named Entity Recognition (NER): Identifying entities like people, locations, etc., within text.
- TF-IDF: A technique to measure the importance of words in a document relative to the whole corpus.
- Word2Vec: Representing words in continuous vector spaces, capturing their semantic meaning.
These techniques are essential for preprocessing and feature extraction in NLP tasks.
This project predicts customer churn using machine learning models. The app.py file is the main implementation, which includes:
- Data Preprocessing: Cleaning and preparing data for analysis.
- Feature Selection: Identifying relevant features influencing churn.
- Model Training: Using classification models in ANN to predict churn.
- Model Evaluation: Evaluating model performance using metrics such as accuracy, precision, and recall.
This project helps businesses predict customer churn and enhance retention strategies.
The RNN repository demonstrates Sentiment Analysis using Recurrent Neural Networks (RNNs). This project includes:
- Text Preprocessing: Tokenization, padding, and vectorization of text data.
- Model Architecture: Building an RNN model using RNN.
- Sentiment Classification: Training the model to classify text into sentiment categories such as positive, negative, or neutral.
This project demonstrates the use of RNNs in analyzing sequential data for sentiment classification.
Next Word Prediction Repository
The Next Word Prediction project uses LSTMs (Long Short-Term Memory networks) to predict the next word in a given sequence. Key aspects of the project include:
- Data Preparation: Collecting and preprocessing text data.
- Sequence Modeling: Training an LSTM to predict the next word based on previous words.
- Text Generation: Generating text by predicting one word at a time.
LSTMs are particularly well-suited for sequential tasks like this, where understanding word dependencies over time is crucial.
The GenAI repository introduces Langchain, a framework designed to build applications that leverage Large Language Models (LLMs). This repository includes:
- Langchain Basics: Demonstrating how to chain multiple LLMs and external tools to solve complex NLP problems.
- Langchain Projects: Practical examples of a real-world use-cases developed using Langchain.
This repository serves as a valuable introduction to Langchain for building language-driven applications.
AWS Bedrock & SageMaker Repository
The AWS Deployment repository focuses on deploying machine learning models using AWS services like Bedrock and SageMaker:
- AWS Bedrock: A service to deploy LLMs at scale across various use cases.
- AWS SageMaker: A managed platform for building, training, and deploying machine learning models.
This project showcases how to leverage AWS services to deploy and scale NLP models in production environments.