In this module, we will delve into the basics of NLP, focusing on word embeddings and sentiment analysis. You’ll gain both theoretical knowledge and practical skills through coding exercises, setting the stage for advanced topics. Concepts like GloVe embeddings and transformers will also be introduced to deepen your understanding of modern NLP.

In this module, we will explore the powerful Huggingface library for pre-trained models. Learn to implement and code solutions for a variety of tasks including text summarization, question answering, and named entity recognition. Gain hands-on experience with the library’s robust pipelines and model functionalities.

In this module, we will guide you through finetuning machine learning models to improve their performance. Through coding exercises, you will learn to build simple models, perform exploratory data analysis, and save/load trained models efficiently using Huggingface tools.

In this module, we will explore vector databases, emphasizing their role in handling large-scale datasets. Through theoretical insights and practical coding, you will learn to implement tokenization, build vector databases, and develop multimodal systems to manage and query complex data effectively.

In this module, we will explore the OpenAI API, delving into its architecture and practical applications. You will learn to obtain and configure API keys, implement the OpenAI Python package, and interact with REST APIs. Additionally, we'll cover cost management for effective project budgeting.

In this module, we will uncover the art of prompt engineering, a critical skill in leveraging AI models effectively. Through practical coding sessions, you will learn techniques for creating clear instructions, managing outputs, and optimizing prompts for complex AI tasks.

In this module, we will take a deep dive into advanced prompt engineering methods, introducing innovative techniques to tackle complex reasoning tasks. You will gain hands-on experience with coding examples, exploring self-consistency, tree-of-thought, and self-critique methodologies to elevate AI model capabilities.

In this module, we will introduce Retrieval-Augmented Generation (RAG) and its role in improving AI outputs by integrating external data. Through hands-on coding, you will learn to handle vector databases, manage LLMs, and combine these elements to create robust RAG implementations.

In this module, we will guide you through a capstone project, focusing on the development of a climate change chatbot. You will prepare data, implement vector databases, apply RAG techniques, and integrate these components into a user-friendly web application. This hands-on project solidifies your learning and showcases your skills.

In this module, we will dive into open-source LLMs, discovering their capabilities and potential for customization. Through practical examples, you will learn to implement these models effectively, empowering you to solve diverse NLP challenges with open-source tools.

In this module, we will explore data augmentation techniques, emphasizing their importance in creating robust datasets. Through coding exercises, you will learn methods like random cropping, back-translation, and contextual augmentation to enhance your machine learning workflows.

In this module, we will cover miscellaneous yet vital topics, including an introduction to Claude and the theoretical underpinnings of LLM functions. Practical coding sessions will reinforce these concepts, ensuring a holistic learning experience.

In this concluding module, we will reflect on your learning journey, summarize key takeaways, and provide guidance on further education and career opportunities. Gain insights into leveraging your skills to achieve success in the field of generative AI and NLP.