Natural Language Processing (NLP) with Python
Artificial Intelligence (AI)
COURSE OVERVIEW
This intensive five-day training program is designed to provide participants with a strong foundation in Natural Language Processing (NLP) using Python. The course covers essential concepts such as text preprocessing, sentiment analysis, and text classification, progressing toward advanced deep learning-based NLP techniques including transformers and real-world applications. Participants will gain hands-on experience building NLP pipelines, developing intelligent text-based systems, and implementing practical applications such as chatbots, summarization tools, and classification models.
By the end of the course, learners will be able to design and deploy NLP solutions that process and understand human language effectively.
COURSE OBJECTIVES
By the end of this course, participants will be able to:
- Understand the fundamentals of Natural Language Processing and text analytics.
- Perform text preprocessing including tokenization, stemming, and lemmatization.
- Apply feature extraction techniques for textual data representation.
- Build sentiment analysis and text classification models using Python.
- Understand word embeddings and deep learning approaches for NLP.
- Explore transformer-based architectures for modern NLP applications.
- Develop NLP applications such as chatbots and text summarizers.
- Build and evaluate a complete NLP project pipeline.
Duration: 5 Days / 40 Hours
Delivery Method: Classroom-based, Virtual Instructor Led Training
COURSE OUTLINE
Day 1: NLP Fundamentals
Focus: Understanding text processing and core NLP concepts
- Introduction to Natural Language Processing (NLP)
- Real-world applications of NLP (search engines, chatbots, assistants)
- Text preprocessing pipeline overview
- Tokenization (word-level, sentence-level, subword tokenization)
- Stop words removal and normalization
- Stemming vs Lemmatization
- Introduction to NLP libraries (NLTK, spaCy basics)
- Hands-on: Preprocessing raw text data using Python
Day 2: Text Analytics and Machine Learning for NLP
Focus: Extracting insights from textual data
- Introduction to text analytics
- Sentiment analysis concepts and applications
- Text classification fundamentals
- Bag of Words (BoW) and TF-IDF feature extraction
- N-grams and feature representation techniques
- Building basic NLP machine learning pipelines
- Hands-on: Sentiment analysis model using real datasets
Day 3: NLP with Deep Learning
Focus: Moving from traditional ML to deep learning models
- Introduction to word embeddings (Word2Vec, GloVe, FastText)
- Semantic representation of words and context
- Sequence models overview (RNN, LSTM, GRU)
- Limitations of traditional sequence models
- Introduction to Transformer architecture
- Attention mechanism basics
- Hands-on: Implementing word embeddings for text data
Day 4: Advanced NLP Applications
Focus: Building intelligent NLP systems
- Named Entity Recognition (NER) and its applications
- Text summarization techniques (extractive vs abstractive)
- Question Answering systems overview
- Chatbot development fundamentals
- Rule-based vs AI-driven chatbots
- Introduction to transformer-based NLP models (BERT, GPT overview)
- Hands-on: Building a simple chatbot or NER application
Day 5: NLP Project Development and Best Practices
Focus: End-to-end NLP implementation and evaluation
- Designing an end-to-end NLP pipeline
- Data collection and preparation for NLP projects
- Model evaluation metrics (accuracy, F1-score, precision, recall)
- Error analysis and model improvement techniques
- Deployment considerations for NLP applications
- Best practices for scalable NLP systems
- Final capstone project development and presentation
- Project review and feedback session
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