Python for Natural Language Processing

Python for Natural Language Processing (NLP) Course Outline

Duration: 40–50 Hours
Level: Beginner to Advanced
Delivery Mode: Online / Offline
Target Audience: Data Scientists, Machine Learning Engineers, AI Researchers, and Developers interested in NLP
Prerequisites: Basic knowledge of Python programming is recommended

Module 1: Introduction to Natural Language Processing (NLP)

• What is NLP?
• Applications of NLP in the Real World (Chatbots, Sentiment Analysis, Translation)
• The Role of Python in NLP
• NLP Libraries in Python: NLTK, SpaCy, TextBlob, Gensim, and Hugging Face Transformers
• Setting Up the NLP Environment and IDE (Jupyter Notebooks, VS Code)

Module 2: Text Preprocessing Techniques

• Text Cleaning and Tokenization
• Lowercasing, Removing Punctuation, and Stop Words
• Stemming and Lemmatization Techniques
• Handling Special Characters and Emojis
• Named Entity Recognition (NER) for Extracting Entities from Text
• Bag of Words and TF-IDF (Term Frequency-Inverse Document Frequency)

Module 3: Working with Text Data

• Reading and Writing Text Data (CSV, Excel, JSON, TXT)
• Introduction to Regular Expressions for Text Processing
• Text Normalization and Vectorization Techniques
• Creating Word Clouds for Data Exploration
• Exploring and Visualizing Word Frequencies
• Text Representation with Numpy and Pandas

Module 4: Sentiment Analysis with NLP

• What is Sentiment Analysis?
• Building a Sentiment Analysis Model with Python
• Exploring Sentiment Analysis Datasets (e.g., Movie Reviews, Social Media Data)
• TextBlob for Basic Sentiment Analysis
• Machine Learning Techniques for Sentiment Classification
• Model Evaluation: Accuracy, Precision, Recall, and F1-Score

Module 5: Topic Modeling and Text Classification

• Introduction to Topic Modeling and Its Applications
• Latent Dirichlet Allocation (LDA) for Topic Modeling
• Understanding and Implementing Non-Negative Matrix Factorization (NMF)
• Text Classification Using Supervised Learning Algorithms
• Working with Scikit-learn for Text Classification Models (Naive Bayes, SVM, Logistic Regression)
• Evaluating and Fine-Tuning Text Classification Models

Module 6: Word Embeddings and Advanced Representations

• Introduction to Word Embeddings
• Working with Word2Vec for Word Representation
• Using GloVe (Global Vectors for Word Representation)
• Understanding and Implementing FastText
• Contextual Word Representations using BERT (Bidirectional Encoder Representations from Transformers)
• Sentence and Document Embeddings with Universal Sentence Encoder (USE)

Module 7: Text Generation and Language Modeling

• Introduction to Language Models and Their Applications
• Building a Simple Language Model with N-Grams
• Recurrent Neural Networks (RNN) for Text Generation
• Long Short-Term Memory (LSTM) Networks for Text Sequences
• Introduction to Transformer Models: BERT, GPT (Generative Pretrained Transformers)
• Text Generation using GPT-2 and GPT-3

Module 8: Named Entity Recognition (NER) and POS Tagging

• Introduction to Named Entity Recognition (NER)
• Identifying and Extracting Named Entities from Text
• Part-of-Speech (POS) Tagging Techniques
• Using SpaCy for POS Tagging and Named Entity Recognition
• Building a Custom NER Model with SpaCy and Training with Custom Data

Module 9: Speech Recognition and NLP Applications

• Introduction to Speech Recognition and NLP Integration
• Working with Speech Recognition Libraries (SpeechRecognition, PyAudio)
• Converting Speech to Text and Building Voice-Activated Systems
• Integrating Speech Recognition with NLP for Enhanced Applications
• Building Chatbots using NLP and Speech Recognition

Module 10: Advanced NLP with Deep Learning

• Introduction to Deep Learning for NLP
• Text Classification with Deep Learning using Keras and TensorFlow
• Building a Recurrent Neural Network (RNN) for Text Sequences
• Using Convolutional Neural Networks (CNN) for Text Analysis
• Advanced Deep Learning Architectures for NLP (BERT, GPT-3, T5)
• Fine-Tuning Pre-Trained Models for Specific NLP Tasks (Hugging Face Transformers)

Module 11: End-to-End NLP Project

• Building a Complete NLP Solution: From Data Collection to Model Deployment
• Preprocessing, Model Training, Evaluation, and Optimization
• Deploying NLP Models with Flask or FastAPI
• Case Studies and Real-World Use Cases in NLP (Customer Reviews, Healthcare, Social Media)
• Building a Chatbot or Virtual Assistant using NLP
• Preparing for NLP Job Roles: Interview Tips and Case Studies