Applied NLP and Generative AI

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.

Updated in May 2025. This course now 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 comprehensive course, you will learn how to navigate the essentials of Natural Language Processing (NLP) and develop skills in text preprocessing. By the end of the course, you will be well-versed in NLP terminology, vector models, and various techniques for processing textual data. This course is designed to help you understand how to transform raw text into a usable format for machine learning tasks. The journey begins with an introduction to NLP, where you will explore basic definitions, followed by an in-depth look into the Bag of Words model and Count Vectorizer theory. You’ll also engage in hands-on exercises with code implementations, such as applying Count Vectorizer and TF-IDF to text data. Additionally, the course dives into tokenization, stopwords, stemming, and lemmatization, equipping you with the fundamental tools for any NLP project. As you progress, you'll be introduced to more advanced concepts like vector similarity and neural word embeddings. With these tools, you’ll learn how to represent and analyze text data effectively, measure the similarity between text vectors, and apply neural embeddings for deeper text comprehension. The course also emphasizes the importance of these techniques in multilingual contexts, giving you strategies to handle NLP tasks in different languages. This course is perfect for anyone eager to gain a foundational understanding of NLP and text preprocessing. It is ideal for beginners in data science and machine learning, but prior knowledge of Python and basic programming will be helpful for maximizing your learning experience. This course strikes a balance between theory and practical application, ensuring you gain valuable skills to apply in real-world NLP projects.