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Natural Language Processing (NLP)
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This 5-day course provides a comprehensive introduction to Natural Language Processing (NLP), a field of artificial intelligence focused on enabling computers to understand and process human language. Participants will explore core NLP techniques such as text preprocessing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and text classification.
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Course Duration
5 Days
Course Details
This 5-day course provides a comprehensive introduction to Natural Language Processing (NLP), a field of artificial intelligence focused on enabling computers to understand and process human language. Participants will explore core NLP techniques such as text preprocessing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and text classification. The course emphasizes practical applications and hands-on exercises using popular NLP libraries like NLTK and spaCy in Python. Through real-world case studies, learners will gain insights into how NLP is used in various domains, from chatbots and machine translation to text summarization and information retrieval.
This course focuses on building practical NLP skills and applying them to real-world problems. Participants will learn how to work with text data, clean and preprocess it, and use various NLP techniques to extract meaningful information. The course covers both rule-based and machine learning approaches to NLP, giving participants a well-rounded understanding of the field. By the end of this course, participants will be able to perform common NLP tasks and have the foundation for further study in this rapidly evolving area.
By the end of this course, learners will be able to:
- Preprocess and clean text data.
- Perform tokenization, part-of-speech tagging, and named entity recognition.
- Conduct sentiment analysis.
- Build text classification models.
- Use NLP libraries like NLTK and spaCy.
- Data scientists, machine learning engineers, and AI researchers.
- Individuals who want to learn about NLP and its applications.
- Anyone who works with text data.
Course Outline
5 days Course
- Introduction to Natural Language Processing:
- What is NLP?
- Text preprocessing: Cleaning and normalizing text data.
- Tokenization: Breaking text into words or phrases.
- Practical exercise: Preprocessing text data using Python libraries.
- Part-of-Speech Tagging and Named Entity Recognition:
- Part-of-speech tagging: Identifying the grammatical role of words.
- Named entity recognition: Identifying people, organizations, and locations.
- Practical exercise: Performing part-of-speech tagging and named entity recognition.
- Sentiment Analysis:
- Sentiment analysis: Determining the emotional tone of text.
- Lexicon-based and machine learning approaches to sentiment analysis.
- Practical exercise: Building a sentiment analysis model.
- Sentiment Analysis:
- Text Classification:
- Text classification: Categorizing text into different categories.
- Machine learning models for text classification: Naive Bayes, Support Vector Machines.
- Practical exercise: Building a text classification model.
- Text Classification:
- NLP Applications and Advanced Topics:
- Chatbots and dialogue systems.
- Machine translation.
- Text summarization and information retrieval.
- Practical exercise: Applying NLP techniques to a specific task.
- NLP Applications and Advanced Topics:
