This course covers the core concepts, techniques, and applications of Natural Language Processing, forming the foundation for advanced areas such as NLU, NLG, sentiment analysis, and conversational AI.
Overview
NLP Foundations Training is an introductory program designed to provide a comprehensive understanding of how computers process, analyze, and generate human language. This course covers the core concepts, techniques, and applications of Natural Language Processing, forming the foundation for advanced areas such as NLU, NLG, sentiment analysis, and conversational AI. Participants will gain practical insights into real-world NLP systems used across industries.
Learning Outcomes
- Understand Natural Language Processing (NLP) fundamentals
- Learn text preprocessing and language analysis techniques
- Apply NLP methods in AI applications
- Work with text classification and language models
- Evaluate NLP model performance and accuracy
Duration & Delivery Mode
14 hours
Target Audience
• AI and data science beginners
• Business and technical professionals
• Product managers and analysts
• Students and academic researchers
• Anyone interested in language-based AI systems
Pre-requisites
• Basic understanding of computers and digital systems
• Familiarity with text-based applications and documents
• No prior AI, machine learning, or programming experience required
Skillset Achieved
• Understanding core NLP concepts and terminology
• Identifying key NLP tasks and workflows
• Evaluating NLP models and outputs
• Applying NLP concepts to business use cases
• Understanding challenges and limitations of NLP
Course Outcome
By the end of this training, participants will have a strong foundation in Natural Language Processing concepts and techniques. Learners will be able to understand how NLP systems work, evaluate their outputs, and apply NLP principles when working with language-based AI applications.
Course Outline
Introduction to Natural Language Processing
• What is NLP and why it matters
• NLP vs NLU vs NLG
• Common NLP applications
Text Preprocessing and Language Basics
• Tokenization, stemming, and lemmatization
• Stop words and normalization
• Handling noisy and unstructured text
Text Representation Techniques
• Bag-of-words and TF-IDF
• Word embeddings and semantic similarity
• Contextual representations overview
Core NLP Tasks
• Text classification and categorization
• Named entity recognition
• Part-of-speech tagging
Machine Learning Approaches to NLP
• Supervised and unsupervised NLP models
• Feature engineering for text
• Model training concepts
Deep Learning and Transformers in NLP
• Neural networks for language processing
• Attention mechanisms and transformers
• Pre-trained language models overview
NLP in Real-World Applications
• Search and information retrieval
• Chatbots and conversational systems
• Enterprise and business use cases
Ethics, Bias, and Responsible NLP
• Bias in language data
• Privacy and compliance considerations
• Responsible AI practices
Hands-on NLP Concept Exercises
• Text preprocessing and analysis examples
• Classification and extraction scenarios
• Guided exercises and discussions
Assessment Topics
- Introduction to NLP concepts
- Text preprocessing techniques
- NLP workflows and applications
- Language models and text analytics
- NLP evaluation and optimization
Evaluation
• Participation in hands-on NLP exercises
• Scenario-based NLP analysis assignments
• Knowledge assessment
Course Materials
Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.
Certification
Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in NLP Foundations Training, validating their understanding of Natural Language Processing concepts and applications.
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What Our Students Say
“The course gave me a solid understanding of how NLP works in real systems.”
“Great foundation before moving into NLU and generative AI topics.”
“The explanations of text preprocessing and embeddings were very clear.”
“Well-structured and easy to follow, even without prior AI knowledge.”
“An excellent starting point for anyone entering the NLP and AI space.”