To be Scheduled

Natural Language Processing (NLP)

Learn Natural Language Processing (NLP) to analyze and interpret textual data. Gain hands-on skills in text preprocessing, sentiment analysis, and building NLP models using Python for real-world applications.

To be Scheduled_Weekends Only
Artificial Intelligence & Automation

About this Training

This hands-on training introduces participants to Natural Language Processing (NLP) techniques and their applications. Learners will explore text preprocessing, tokenization, feature extraction, sentiment analysis, and NLP model building using Python libraries such as NLTK, SpaCy, and Hugging Face Transformers. Emphasis is placed on practical exercises and real-world datasets, enabling participants to analyze textual data, extract insights, and deploy NLP solutions. By the end of the course, learners will be capable of implementing NLP workflows for research, business intelligence, and AI applications.

What You'll Learn

  • Module 1: Introduction to NLP – Basics applications and NLP workflow
  • Module 2: Text Preprocessing – Tokenization stop words stemming and lemmatization
  • Module 3: Feature Extraction – Bag-of-Words TF-IDF and embeddings
  • Module 4: Sentiment Analysis – Analyzing text sentiment and opinion mining
  • Module 5: NLP Modeling – Classification named entity recognition and topic modeling
  • Module 6: Advanced NLP with Transformers – Using pre-trained models for text tasks
  • Module 7: Evaluation & Optimization – Metrics model tuning and performance improvement
  • Module 8: Real-World NLP Projects – Implementing NLP workflows on sample datasets

This training includes:

Hands-on exercises in text preprocessing
Feature extraction and vectorization
Sentiment analysis projects
NLP model building with Python
Using advanced transformers for text tasks
Model evaluation and optimization
Real-world NLP project implementation

Skills you'll gain:

Text preprocessing and cleaning
Feature extraction techniques
Sentiment analysis
NLP model building
Named entity recognition and classification
Working with transformer models
Model evaluation and tuning
Applying NLP to real-world datasets