Natural Language Processing with Python
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Next Start
September 28, 2026
(Offered monthly)Duration
5 Days
ClassroomProgram Fee
$1,500
Exclusive 16% VATOther Dates
October 26, 2026
November 23, 2026
November 23, 2026
Course Description
This comprehensive course provides a practical introduction to Natural Language Processing (NLP) using Python and modern libraries. Designed for learners who already have foundational machine learning knowledge, the course focuses on transforming, analyzing, and extracting insights from text data using both traditional and deep learning-based NLP techniques. Participants will develop the skills to build real-world NLP applications for sentiment analysis, text classification, named entity recognition, and more.
The curriculum covers text preprocessing, tokenization, embeddings, traditional NLP methods, transformers, and modern NLP architectures. Using libraries such as spaCy, NLTK, scikit-learn, and Hugging Face Transformers, learners will work through hands-on exercises and projects that simulate real-world NLP workflows. By the end of the course, participants will be able to preprocess text data, build NLP pipelines, fine-tune transformer models, and deploy NLP applications for practical use cases.
Prerequisite note: This course requires foundational machine learning knowledge and Python programming experience. Participants should be comfortable with supervised learning concepts and model evaluation. No prior NLP experience is necessary.
Course Outline
This course is structured into 10 core modules, each carefully designed to build progressively on the previous one.
Learning Outcomes
Technical Skills
- Preprocess and clean text data for NLP tasks.
- Apply tokenization, stemming, and lemmatization.
- Build NLP pipelines with spaCy and NLTK.
- Create text embeddings with Word2Vec, GloVe, and BERT.
- Implement sentiment analysis and text classification.
- Perform Named Entity Recognition (NER) and Part-of-Speech (POS) tagging.
- Fine-tune transformer models (BERT, RoBERTa) with Hugging Face.
- Build text generation and summarization applications.
- Apply topic modeling and text clustering techniques.
- Deploy NLP models as REST APIs.
Career Benefits
- Build in-demand NLP skills used across multiple industries.
- Develop the ability to extract insights from unstructured text data.
- Gain hands-on experience with spaCy, Hugging Face, and modern NLP tools.
- Prepare for advanced topics such as LLMs and generative AI.
- Enhance your portfolio with real-world NLP projects.
- Improve employability for AI, ML, and NLP roles.
- Develop skills applicable to healthcare, finance, e-commerce, and social media.
Who Should Attend
- Aspiring NLP Engineers — Individuals building NLP skills.
- Data Scientists — Professionals adding NLP to their toolkit.
- Machine Learning Engineers — Individuals working with text data.
- Researchers — Academics applying NLP to research problems.
- Software Engineers — Developers adding AI capabilities to applications.
- Students & Career Changers — Learners preparing for AI-focused careers.
- Product Managers — Leaders building NLP-powered products.
- Analytics Professionals — Anyone interested in text analytics and language understanding.
Prerequisites & Requirements
Prerequisites
- Applied Machine Learning with Python or equivalent knowledge.
- Understanding of machine learning fundamentals (supervised learning, model evaluation).
- Fundamental Python programming knowledge.
- Basic understanding of deep learning concepts is helpful but not required.
- Comfortable working with tabular and text data.
- No prior NLP experience is necessary.
Requirements
- Laptop with at least 2GB RAM (GB recommended for smoother performance).
- All learning facilities are provided on-site, including high-speed internet, projectors, writing materials, and refreshments.
- Administrative rights on your laptop to install R, RStudio, and necessary packages.
- Sufficient free disk space (at least 2GB) for software installation and project files.
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