Natural Language Processing Fundamentals: Techniques, Tools, and Applications

This comprehensive course is designed to lay a solid foundation in Natural Language Processing (NLP), a discipline that bridges the gap between human language and computer understanding, and is a cornerstone in the world of data science and artificial intelligence. The ability to analyze and derive meaningful insights from textual data is a highly sought-after skill.

This course serves as a bridge for professionals and students to transition into roles that require NLP expertise. With the help of course instructor and NLP specialist, Lisa Becker, learners will acquire skills such as text analysis and feature engineering. They will work with various language models and understand the underlying technologies which will prepare them to tackle real-world challenges and innovate various sectors including healthcare, finance, e-commerce and beyond. This is not just a skill-enhancement course, but a pathway to unlocking a plethora of opportunities in the rapidly expanding field of AI and machine learning by studying the basics of more traditional NLP techniques as well as the big milestones such as transformers and large language models.

What you’ll learn and how to apply it

By the end of this course, you will…

• Have a well-rounded understanding of Natural Language Processing, from its linguistic foundations to its latest advancements and applications.
• Acquire hands-on skills in text processing, feature engineering, and the usage of various NLP models, including a deep dive into transformer architectures.
• Be equipped with both theoretical knowledge and practical skills, you will be prepared to apply NLP techniques to real-world problems across diverse industries, while also understanding the ethical implications of this technology.

This course is for you because

• You're a data scientist or ML engineer with foundational knowledge in data science and, programming, who wants to learn the essential NLP techniques.
• You're a student in computer science, linguistics, or data science with an interest in diving deeper into specialized areas of machine learning to incorporate into your work or studies.

Prerequisites

• Python Programming: Basic to intermediate knowledge of Python is essential since the course involves hands-on exercises and code walkthroughs.
• Jupyter Notebooks: Familiarity with using Jupyter notebooks, as the hands-on exercises and demos will be conducted within this environment. Access the course’s notebooks in the accompanying GitHub repository.
• Data Science Techniques: Basic understanding of data preprocessing and statistical analysis.
• Machine Learning Foundations: A foundational grasp of core machine learning concepts, including training/testing datasets and model evaluation metrics. Basic understanding of neural networks preferred, but not required.