What's included

• Natural Language Processing (NLP) Development

  : Python, known for its rich ecosystem of libraries and frameworks for NLP. Libraries and Frameworks: NLP: Natural Language Toolkit (NLTK), spaCy for natural language processing tasks, including tokenization, stemming, lemmatization, and parsing. Deep Learning for NLP: Transformers library (providing access to models like BERT, GPT, T5), TensorFlow, and PyTorch for building and training state-of-the-art NLP models. Text Vectorization: Gensim for topic modeling and document similarity analysis, and Scikit-learn for feature extraction and text classification. NLP Services I Offer: Text Analysis and Sentiment Analysis: Development of models to analyze text data from various sources (social media, reviews, customer feedback) to determine sentiment, trends, and customer opinions. Chatbots and Conversational Agents: Design and implementation of intelligent chatbots and conversational agents for customer support, e-commerce, and interactive user experiences. Named Entity Recognition (NER): Development of models to identify and categorize key information in text such as names, organizations, locations, dates, and other specifics. Text Classification and Categorization: Automated classification of text into predefined categories, useful for content filtering, organization, and recommendation systems. Machine Translation: Implementation of machine translation solutions to automatically translate text between languages with high accuracy. Natural Language Generation (NLG): Generating human-like text from structured data, useful for automated report generation, content creation, and summarization tasks. Topic Modeling and Keyword Extraction: Extracting topics and keywords from large volumes of text to uncover hidden themes and improve content discoverability. Speech Recognition and Generation: Development of speech processing solutions to convert speech to text and vice versa, enabling voice-activated applications and services. Development and Collaboration Tools: IDEs and Notebooks: Jupyter Notebook, Visual Studio Code, and PyCharm for code development and testing. Version Control: Git and GitHub for source code management and collaboration. Deployment and Cloud Platforms: Experienced in deploying NLP models. Experience with Docker for containerization and Kubernetes for orchestration to facilitate scalable and efficient deployment of NLP services.