Zephyr - Natural Processing Language Course Details

Natural Processing Language

Description

A Natural Language Processing (NLP) course is designed to teach students about the tools and techniques used to process, analyze, and generate natural language text data. The course typically covers topics such as language models, text classification, sentiment analysis, named entity recognition, and machine translation.

Throughout the course, students will work on hands-on projects that will help them develop their skills and confidence in NLP. By the end of the course, students should be able to design and develop their own NLP applications and have a solid understanding of NLP concepts and best practices. They will also be prepared to work as an NLP developer or consultant in various industries such as healthcare, finance, and marketing.

The Course Curriculam

Introduction to Natural Language Processing
Understanding the basics of natural language processing
Applications of NLP in real-life scenarios
Text preprocessing and cleaning
Understanding the importance of feature engineering
Techniques for feature extraction from text data
Text representation techniques such as bag-of-words and TF-IDF
Exploring word embeddings like Word2Vec and GloVe
Text Classification
Introduction to text classification
Understanding different types of classifiers like Naive Bayes, SVM, and neural networks
Evaluating performance metrics for classifiers
Tuning hyperparameters for text classification models
Named Entity Recognition and Information Extraction
Understanding named entity recognition and its importance
Creating a named entity recognition system using CRF and other techniques
Evaluating the performance of named entity recognition systems
Sentiment Analysis
Creating a sentiment analysis system using different techniques
Advanced Topics in Natural Language Processing
Machine translation
Chatbot development using NLP
Future trends in natural language processing

Online-Offline

Online - 2hour -3hour/day (monday-friday)

Live classes are provided for those who choose online mode

Evening special batches for students

Offline -2 hour-3 hour/day(monday-friday)

Classroom sessions for offline mode

Prajnesh N
22 April, 2022
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26 April, 2022
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