Python/RAG for AI/ML/LLM/NLP
Ketan Parmar
Starting at
$
10
/hrAbout this service
Summary
As a Freelance Senior Machine Learning Engineer specializing in Natural Language Processing (NLP), I offer a wide range of services designed to help clients leverage the power of NLP to extract insights, automate processes, and enhance their products and services. My expertise encompasses the latest advancements in NLP technologies, ensuring that clients receive state-of-the-art solutions tailored to their needs.
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.
Skills and tools
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