The Natural Language Processing (NLP) Techniques course is your comprehensive guide to understanding how machines process and interpret human language.
Natural Language Processing (NLP) Techniques
The Natural Language Processing (NLP) Techniques course is your comprehensive guide to understanding how machines process and interpret human language. With language being the core of communication, NLP serves as a bridge between humans and machines—enabling applications like chatbots, voice assistants, translation tools, and sentiment analysis systems.
This course explores essential Natural Language Processing (NLP) Techniques from foundational to advanced levels. You will learn how raw text is transformed into structured data, how machines classify, cluster, and understand language, and how state-of-the-art models like Transformers are revolutionizing AI. While this course focuses on computational NLP, we also explore how Natural Language Processing Neuro Linguistic Programming intersects with technology to enhance human-machine interaction through psychological modelling of human language and behaviour.
Whether you aim to build smarter AI systems or simply understand the science behind Siri, ChatGPT, or Google Translate, this course offers a deep yet accessible dive into the world of language and machines.
This course is ideal for students, developers, data analysts, and AI enthusiasts who are interested in exploring how machines understand and generate human language. It is particularly valuable for those looking to specialize in AI, machine learning, or data science, as well as professionals in linguistics or psychology interested in the overlap between Natural Language Processing and Neuro Linguistic Programming. A basic understanding of programming (preferably Python) will help learners make the most of this course, but no prior NLP experience is required.
Understand the core concepts of Natural Language Processing (NLP) Techniques.
Preprocess and represent text data for machine learning.
Perform text classification, clustering, and topic modelling.
Extract useful information using Named Entity Recognition (NER).
Conduct sentiment analysis and opinion mining on text data.
Apply part-of-speech tagging and syntactic parsing.
Explore advanced NLP techniques like Transformers and Attention Mechanisms.
Discover real-world applications of NLP across industries.
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Understand the history, significance, and evolution of NLP. Explore key concepts and how NLP powers modern AI systems.
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Learn to clean and transform text data using tokenization, stop-word removal, stemming, lemmatization, and vectorization methods like TF-IDF and word embeddings.
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Explore supervised and unsupervised learning techniques to classify and group documents. Learn algorithms such as Naive Bayes, SVM, and K-Means for NLP tasks.
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Identify and extract structured information such as names, places, and dates from text using NER models and rule-based systems.
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Analyse the emotional tone behind texts using lexical and machine learning approaches. Apply techniques to social media, reviews, and customer feedback.
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Dive into grammatical structure and syntactic analysis. Learn POS tagging and parse trees to understand sentence structure and dependency parsing.
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Master cutting-edge models such as BERT and GPT. Understand how attention mechanisms improve context understanding in text.
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Discover how NLP is used in chatbots, virtual assistants, healthcare, finance, and more. Analyse successful case studies and practical implementations.
Earn a Professional Certificate
Earn a certificate of completion issued by Learn Artificial Intelligence (LAI), recognised for demonstrating personal and professional development.
Shadman Raees
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No, this course starts from the basics. A basic understanding of Python programming is helpful but not mandatory.
Yes. While the course focuses on computational NLP, it also introduces the concept of Natural Language Processing Neuro Linguistic Programming, showing how language models relate to human communication strategies.
Absolutely! Each module includes practical exercises and mini-projects to apply what you’ve learned.
Yes, in the advanced module, you will explore models based on Transformers and understand the mechanisms behind popular NLP tools like ChatGPT.
Definitely. NLP skills are in high demand across industries. Completing this course gives you a strong foundation for roles in AI, data science, and machine learning.
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