Course Syllabus

Module 1: Introduction to NLP and Python

This module introduces the core concepts of NLP and shows how Python can be used for language processing. It covers the NLP pipeline (tokenization, parsing, etc.), common NLP tasks (e.g. classification, translation, sentiment analysis), and real-world examples (voice assistants, search engines). Participants set up the Python environment and explore basic text handling with libraries like NLTK. By the end, learners will have run simple NLP scripts (e.g. tokenizing text and computing word frequencies) and understood how NLP enables applications such as chatbots and translators.

• Overview of NLP tasks and use-cases (speech-to-text, chatbots, search).
• Python text handling: strings, tokenization, regular expressions.
• Introduction to NLTK and accessing text corpora.
• Hands-On Lab: Simple text analysis (word count, frequency distribution on sample dataset).

Module 2: Text Preprocessing and Traditional Techniques

Building on Module 1, this module dives into fundamental text preprocessing steps that clean and normalize text data. Topics include tokenization (splitting text into words/sentences), lowercasing, removing punctuation, stop words, stemming and lemmatization. These traditional NLP techniques prepare raw text for analysis Participants use Python libraries (NLTK, spaCy) to implement preprocessing pipelines. The module emphasizes industry relevance by applying preprocessing to real-world data (e.g. tweets, customer reviews).

• Tokenization methods (word and sentence tokenizers).
• Text normalization: casing, punctuation removal, stopword filtering.
• Stemming vs. Lemmatization (NLTK Porter stemmer, WordNet lemmatizer).
• Bag-of-Words model basics: splitting text into tokens.
• Hands-On Lab: Clean and preprocess a raw text dataset (remove noise, tokenize, lemmatize).

Module 3: Text Representation and Vectorization

This module covers how to convert text into numerical features that machine learning models can consume. It introduces the Bag-of-Words and TF-IDF (term frequency–inverse document frequency) models for text representation, as well as n-gram features. Participants learn to build document-term matrices using scikit-learn’s feature extraction tools. The module highlights practical concerns such as feature dimensionality and sparse data. By the end, learners can transform text corpora into vectorized datasets ready for modeling.

• Bag-of-Words (BoW) representation: vocabulary, word counts.
• TF-IDF weighting to reflect importance of terms.
• N-grams (bi-grams, tri-grams) for capturing phrases.
• Feature selection (e.g. limiting vocabulary size).
• Hands-On Lab: Build BoW and TF-IDF representations for a text corpus and visualize term statistics.

Module 4: Text Classification and Evaluation

Focusing on a key application, this module teaches how to classify text using machine learning algorithms. Topics include supervised models like Naive Bayes, logistic regression, and support vector machines applied to text features. Learners implement a complete text classification pipeline: feature extraction (from Module 3), model training, and evaluation. The module covers evaluation metrics (accuracy, precision, recall, F1-score) and use of confusion matrices. A practical case study on sentiment analysis or spam detection gives context.

• Supervised classifiers for text: Multinomial Naive Bayes, Logistic Regression, SVM.
• Model evaluation: train/test split, cross-validation.
• Performance metrics: accuracy, precision/recall, F1-score.
• Hands-On Lab: Build and evaluate a sentiment classifier on movie reviews or a spam detector for emails.

Module 5: Sequence Labeling and Information Extraction

This module explores sequence-based NLP tasks that label each word/token in text. Topics include Part-of-Speech (POS) tagging, chunking (shallow parsing), and Named Entity Recognition (NER) for identifying proper names, organizations, dates, etc. Both rule-based and machine learning approaches (e.g. conditional random fields, spaCy pre-trained models) are covered. Participants practice extracting structured information from text (e.g. extracting names and places from news articles). The emphasis is on practical implementation using Python libraries.

• POS tagging (assigning grammatical tags to words).
• Chunking and shallow parsing of noun/verb phrases.
• Named Entity Recognition (NER) with spaCy or NLTK’s NE chunker.
• Rule-based vs. statistical NER (overview of CRF approach).
• Hands-On Lab: Extract named entities from a dataset of news articles or social media posts.

Module 6: Word Embeddings and Semantic Analysis

This module introduces dense vector representations of words (word embeddings) that capture semantic meaning. Topics include Word2Vec and GloVe algorithms, how to train embeddings on a corpus, and how to use pre-trained embeddings. Participants learn to compute word similarity and analogies, and to use embeddings as features in downstream tasks. The practical focus includes using libraries like Gensim to train Word2Vec or load GloVe vectors, and applying semantic similarity in an application (e.g. finding similar words or documents).

• Introduction to word embeddings: dense vs. sparse vectors.
• Word2Vec (CBOW and Skip-gram) and GloVe fundamentals.
• Training custom embeddings vs. using pre-trained (e.g. GoogleNews vectors).
• Semantic tasks: cosine similarity, word analogies.
• Hands-On Lab: Train word embeddings on a text corpus and use them to find similar words or to cluster documents.

Module 7: Neural Networks for NLP

Moving into deep learning, this module covers how neural networks can model sequential text data. It introduces recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent unit (GRU) architectures for language modeling and sequence classification. Participants use a Python deep learning framework (TensorFlow/Keras or PyTorch) to build and train a simple RNN/LSTM for an NLP task (e.g. sentiment analysis or sequence prediction). The module also touches on word embedding layers and text preprocessing for neural models.

• Neural network basics for sequences: RNN, LSTM, GRU architectures.
• Preparing text for deep learning (tokenization, padding, embedding layers).
• Training sequence models for text classification or prediction.
• Overfitting and regularization (dropout, early stopping).
• Hands-On Lab: Implement an LSTM-based text classifier (e.g. classify movie reviews or tweets) and evaluate its performance.

Module 8: Transformers and State-of-the-Art NLP

This module presents modern transformer-based models that have revolutionized NLP. It explains the attention mechanism and Transformer architecture behind models like BERT (Bidirectional Encoder Representations from Transformers) and GPT. Participants learn how to use pre-trained transformer models (via Hugging Face’s Transformers library) for tasks such as text classification, question answering, or text generation. The module includes fine-tuning a pre-trained model (e.g. BERT) on a sample dataset. Real-world applications (e.g. using BERT for sentiment or chatbot) illustrate the state-of-the-art capabilities.

• Attention mechanism and Transformer architecture overview.
• Pre-trained language models: BERT, GPT, RoBERTa, etc.
• Fine-tuning transformers for specific tasks (using Hugging Face).
• Example applications: text summarization, question answering, chatbots.
• Hands-On Lab: Fine-tune a BERT model on a custom dataset (e.g. sentiment or topic classification) and evaluate its accuracy.

Module 9: Applications, Case Studies, and Capstone Project

The final module ties everything together with industry-focused case studies and a capstone project. Learners explore real-world NLP applications such as chatbots (customer service bots), text summarization (news or legal documents), and machine translation. The module discusses best practices for deploying NLP solutions (data pipelines, model serving) and ethical considerations (bias in language models). Participants work on a capstone project that synthesizes multiple techniques (e.g. building an end-to-end NLP pipeline or an interactive chatbot). This hands-on project and group presentation demonstrate mastery of course topics.