Dive into the transformative world of Natural Language Processing (NLP) with this comprehensive course designed to equip you with modern skills and tools. From foundational principles to advanced techniques, you’ll develop a thorough understanding of NLP, including pre-trained models, vector databases, and prompt engineering.
The course begins with essential system setups and an introduction to NLP, where you'll explore concepts like word embeddings, sentiment analysis, and transformers. Progressing further, you'll learn to leverage powerful tools like Huggingface for pre-trained models, experiment with various applications such as text summarization, translation, and question answering, and build skills in model finetuning with hands-on coding exercises.
Next, you’ll delve into vector databases and data augmentation, uncovering techniques for managing and enhancing large datasets. Advanced topics such as retrieval-augmented generation (RAG), open-source LLMs, and sophisticated prompt engineering will prepare you for cutting-edge AI applications. Finally, you’ll apply your knowledge to a capstone project, creating a climate change chatbot using state-of-the-art methods.
This course is ideal for intermediate learners with a basic understanding of Python and machine learning. Whether you’re an AI enthusiast, a data scientist, or a professional looking to enhance your NLP expertise, this course will take your skills to the next level.
Overview
Syllabus
- Course-Introduction
- In this module, we will introduce the course structure, objectives, and the instructors. You will learn how to navigate the course effectively, access materials, and prepare your system for hands-on coding exercises. This foundational setup ensures a smooth learning experience throughout the course.
- NLP-Introduction
- 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.
- Apply Huggingface for Pre-Trained Models
- 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.
- Model Finetuning
- 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.
- Vector Databases
- 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.
- OpenAI API
- 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.
- Prompt Engineering
- 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.
- Advanced Prompt Engineering
- 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.
- Retrieval-Augmented Generation (RAG)
- 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.
- Capstone Project "Chatbot"
- 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.
- Open Source LLMs
- 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.
- Data Augmentation
- 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.
- Miscellaneous
- 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.
- Final Section
- 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.
Taught by
Packt - Course Instructors
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