About

This program is designed to give you a thorough understanding of Natural Language Processing (NLP) and how it’s applied in real-world scenarios. You’ll start by learning the basics like text preprocessing, tokenization, and sentiment analysis. Then, you’ll dive into advanced topics such as topic modeling, sequence models, and state-of-the-art transformer models like BERT. By the end, you’ll have the skills to work with key NLP tools and libraries, setting you up for further studies or a career in NLP and AI.

Program Objectives

• Understand the core concepts and techniques of NLP.
• Learn how to clean, preprocess, and normalize text data.
• Implement tokenization, stemming, and lemmatization.
• Build and evaluate sentiment analysis models.
• Explore topic modeling techniques like LDA.
• Develop sequence models using RNNs and LSTMs.
• Work with advanced transformer models like BERT.
• Get hands-on experience with libraries like NLTK, spaCy, and Hugging Face Transformers.
• Complete real-world NLP projects to demonstrate your skills.
• Prepare for advanced roles in NLP and AI through practical training and hands-on work.

Program Structure

Introduction to NLP

• Get an overview of NLP and where it’s used.
• Learn key concepts and important terminology.
• Discover how to clean and preprocess text data.

Text Processing and Tokenization

• Master techniques for normalizing text.
• Learn different ways to tokenize text.
• Understand stemming and lemmatization.

Sentiment Analysis

• Understand the basics of sentiment analysis.
• Build your own sentiment analysis models.
• Learn how to evaluate the performance of these models.

Topic Modeling

• Get introduced to topic modeling.
• Learn how to apply Latent Dirichlet Allocation (LDA).
• Implement topic modeling in Python.

Sequence Models

• Understand how sequence data works.
• Dive into Recurrent Neural Networks (RNN).
• Learn to work with Long Short-Term Memory (LSTM) networks.

Transformer Models

• Get introduced to transformer models and how they work.
• Learn about BERT (Bidirectional Encoder Representations from Transformers).
• Implement transformers for various NLP tasks.

Practical NLP

• Use Python and Jupyter Notebooks to solve NLP problems.
• Work with popular NLP libraries like NLTK and spaCy.
• Build advanced models using Hugging Face Transformers.

Who Should Enroll

• Senior undergraduate and graduate students in Computer Science or related fields.
• IT professionals, data scientists, and software developers looking to upskill in NLP.

What You’ll Gain

• A solid understanding of NLP principles and methods.
• The ability to clean, preprocess, and analyze text data.
• Hands-on experience with building sentiment analysis models.
• Practical skills in topic modeling, sequence models, and transformers like BERT.
• Mastery of popular NLP libraries like NLTK, spaCy, and Hugging Face Transformers.
• The confidence to apply NLP techniques to real-world problems.
• Sharpened Python skills to handle advanced NLP tasks.
• A certificate of completion recognized by leading companies in the industry.

What You’ll Get

• Access to an e-LMS platform for all course materials.
• Real-time projects for your dissertation or portfolio.
• Guidance from experienced mentors.
• Opportunities to publish papers.
• Self-assessment tools to track your progress.
• A final exam to test your skills.
• An e-Certificate and e-Marksheet upon completion.

Career Opportunities

• NLP Engineer: Build and implement NLP models and tools.
• Data Scientist: Work with text data to gain insights and support business decisions.
• AI Research Scientist: Conduct cutting-edge research in NLP.
• Machine Learning Engineer: Develop and fine-tune machine learning models for text data.
• AI Product Manager: Lead the creation of AI-driven products with NLP.
• Computational Linguist: Bridge the gap between linguistics and technology to improve language-based applications.