Natural Language Processing Specialization Course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Introduction to Natural Language Processing

Estimated time: 20 hours

• Understand the basics of NLP and its role in AI and data science
• Learn text preprocessing techniques
• Perform tokenization and text normalization
• Apply part-of-speech tagging using NLTK and SpaCy

Module 2: Text Classification & Sentiment Analysis

Estimated time: 30 hours

• Apply machine learning algorithms for text classification
• Use TF-IDF and bag-of-words models
• Build a sentiment analysis model using Python and Scikit-Learn
• Evaluate model performance with real-world datasets

Module 3: Deep Learning for NLP

Estimated time: 40 hours

• Explore neural networks and sequence models for NLP
• Understand word embeddings like Word2Vec and GloVe
• Implement recurrent neural networks (RNNs) for text data
• Study transformers, BERT, and GPT for state-of-the-art NLP

Module 4: Advanced NLP Applications

Estimated time: 50 hours

• Build chatbots using sequence-to-sequence models
• Develop machine translation systems
• Create text summarization tools
• Use Hugging Face Transformers and TensorFlow/PyTorch

Module 5: Capstone Project: Real-World NLP Application

Estimated time: 60 hours

• Work on a hands-on NLP project using real-world datasets
• Develop a full NLP pipeline from preprocessing to modeling
• Deploy a working NLP application

Prerequisites

• Basic knowledge of Python programming
• Familiarity with machine learning concepts
• Understanding of fundamental AI principles

What You'll Be Able to Do After

• Process and analyze text data using industry-standard tools
• Build and train machine learning models for NLP tasks
• Apply deep learning techniques like RNNs and transformers
• Develop real-world applications such as chatbots and translators
• Earn a certificate to showcase NLP expertise