Applied Generative AI & NLP with Python

Coursera MOOC / Non-credit USD 49
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Applied Generative AI & NLP with Python

About this course

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, learners will dive deep into the world of generative AI and natural language processing (NLP) using Python. With a focus on hands-on coding, the course will guide you through creating powerful NLP applications, from sentiment analysis to text classification and question-answering systems. You'll work with popular frameworks such as Huggingface and OpenAI, while also learning techniques like word embeddings, transformers, and model fine-tuning. By the end of the course, you’ll have the skills to create state-of-the-art NLP applications and deploy them in real-world scenarios. The course begins with foundational knowledge in NLP, including sentiment analysis and word embeddings using techniques such as GloVe. It progresses to more advanced models like transformers, Huggingface pipelines, and pre-trained models, before diving into the intricacies of model fine-tuning, data augmentation, and retrieval-augmented generation (RAG). Additionally, learners will be guided through implementing and deploying applications, including a climate change chatbot using RAG and vector databases. This course is ideal for individuals eager to explore the growing field of generative AI and NLP. It is suitable for anyone with basic Python knowledge and an interest in machine learning, data science, or AI. No prior experience in NLP or deep learning is required, making it accessible to beginners as well as more experienced developers looking to broaden their skillset.

What you'll learn

  • Build sentiment analysis and text classification systems using Python
  • Implement word embeddings with techniques like GloVe
  • Work with transformer models and Huggingface pipelines
  • Fine-tune pre-trained language models for specific tasks
  • Create question-answering systems using retrieval-augmented generation (RAG)
  • Deploy NLP applications using vector databases
  • Apply data augmentation techniques to improve model performance

Course objectives

  • Develop hands-on skills in building state-of-the-art NLP applications
  • Learn to use popular frameworks including Huggingface and OpenAI
  • Understand and implement advanced techniques like transformers and model fine-tuning
  • Deploy real-world NLP applications including chatbots with RAG architecture

Skills you'll gain

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