I am a freelance machine learning engineer with over 4 years of experience in developing and deploying advanced machine learning NLP solutions. I have a proven track record of helping businesses of all sizes to improve their efficiency, productivity, and profitability with machine learning NLP solutions.

I specialize in developing Python-based machine learning NLP solutions for a variety of tasks, including:

• Natural language processing (NLP): I can help you to develop solutions that can understand and generate human language. This includes                tasks such as text classification, sentiment analysis, and machine translation.

• Question answering: I can help you to develop solutions that can answer questions posed in natural language.
• Chatbots: I can help you to develop chatbots that can interact with users in a natural and engaging way.
• Text summarization: I can help you to develop solutions that can summarize text in a concise and informative way.

Why hire me?

• I have a deep understanding of machine learning and NLP principles and techniques.
• I am proficient in Python, the most popular language for machine learning NLP development.
• I have a proven track record of success in developing and deploying machine learning NLP solutions for a variety of businesses.
• I am committed to delivering high-quality solutions that meet my clients' specific needs.

Tools:

• NLTK (Natural Language Toolkit): NLTK provides easy-to-use interfaces and resources for text processing and linguistic data.
• spaCy: spaCy is known for its speed and efficiency. It offers pre-trained models for various NLP tasks, including named entity recognition and part-of-speech tagging.
• Gensim: Gensim is used for topic modeling and document similarity analysis. It's often used for tasks like text summarization and document clustering.
• Transformers: This library provides pre-trained models like BERT, GPT-3, and RoBERTa for various NLP tasks, including sentiment analysis, text generation, and translation.
• TextBlob: TextBlob is easy-to-use library that offers basic NLP functionalities, such as part-of-speech tagging, noun phrase extraction, and sentiment analysis.
• Stanford NLP: Stanford NLP provide a wide range of NLP capabilities, including tokenization, named entity recognition, and dependency parsing. It's known for its accuracy.
• Pattern: Pattern offers tools for web mining, natural language processing, machine learning, and network analysis.
• PyTorch and TensorFlow: These deep learning frameworks can be used for advanced NLP tasks, such as training custom neural networks for sentiment analysis and language modeling.
• Word2Vec and Doc2Vec: These models, available in Gensim and other libraries, are used for word and document embeddings, which are crucial for various NLP tasks.
• Stanford CoreNLP: It provides a wide array of NLP tools and models, including tokenization, part-of-speech tagging, and coreference resolution.