Beginner’s Guide to Natural Language Processing
Take this course at your own pace through pre-recorded video and online resources.
Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that concerns how human languages interact with computers. NLP has seen some of the most rapid development of any field of AI, resulting in an explosion of research, development and productionisation.
Huge volumes of data exist not as numerical information, but as written or spoken language. Interacting with data in this form has been a longstanding challenge within. From smart assistants to generative digital art, some of the most visible interactions people have with AI-enabled systems are underpinned by Natural Language Processing.
With an initial intuition underpinned by a Distributional hypothesis – that the meaning of words can be understood by their surroundings – advances in deep learning algorithms and accelerated hardware have resulted in a new generation of powerful statistical models that are able to match, or even outperform Humans on many benchmarks.
Studying NLP forces us to consider deep concepts like the nature of “meaning” and “understanding” and leads us to the intersection between other fields of AI and Computer Science more generally, such as Knowledge Engineering, Computer Vision, Joint Representation Learning, Speech Recognition and many more.
The interconnectedness of this topic and proliferation of tools has resulted in a bewildering landscape of off-the-shelf tools, open source libraries, standalone applications and scarcely believable claims.
This course will cover the fundamental concepts of NLP through to the applications of the resulting tools and technologies. Case studies demonstrating the use-cases and benefits of applying NLP will also be presented. Attendees will be able to learn from our experience in embedding NLP to solve business challenges and hear about real-life case studies where NLP has benefitted our partner organisations.
- What is meant by “NLP”
- Common applications of NLP
- Commonly used NLP
- Frameworks and Libraries NLP “tasks” and how they map to production use-cases
- “Classical” vs “Contemporary” Natural Language Processing
- Limitations and challenges
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