NLP and Transformer Models

Understand how machines process human language. This course will teach you the fundamentals of NLP and Transformer models, and how to apply them using Python to solve real-world language processing problems.

What you’ll learn

NLP sits at the core of many AI-powered tools you use today, such as chatbots, voice assistants, translation, and summarization engines. Understanding how natural language processing (NLP) works under the hood can be challenging without a structured introduction. In this course, NLP and Transformer Models, you’ll gain the foundational skills to confidently work with modern NLP techniques and Transformer architectures.

First, you’ll explore the basics of natural language processing and understand how text data is processed through rule-based systems and machine learning models. You’ll compare traditional techniques with modern ML-driven approaches. Next, you’ll dive into the key components of Transformer models, including self-attention, multi-head attention, and feedforward neural networks, and learn how these architectures revolutionized NLP tasks. Finally, you’ll apply these concepts through a hands-on Python demo, building a Transformer-based solution to a real-world NLP problem.

By the end of this course, you’ll have a solid understanding of how NLP workflows and the power of Transformer models and be ready to build your own text processing applications with Python.

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