Natural Language Processing: Course Overview

About Natural Language Processing

Natural language processing is the field of teaching computers to read, understand, and generate human language. This course starts with the Introduction to NLP — what makes language hard for machines and where NLP shows up in products you use every day — then moves straight into Text Preprocessing, where you learn to clean and tokenize raw text.

Next you'll learn how to turn words into numbers. Text Representation covers bag-of-words and TF-IDF, Language Models & N-grams introduces predicting the next word, and Word Embeddings shows how models like Word2Vec and GloVe capture meaning as vectors. With these foundations you'll build real applications in Text Classification and Sequence Labeling (part-of-speech tagging and named entity recognition).

The course then moves into the neural era: RNNs & LSTMs for sequential data, followed by Attention & Transformers, the architecture behind modern language models. You'll put that to work with Pretrained Models & Fine-Tuning using BERT and the Hugging Face ecosystem, then tackle sequence-to-sequence tasks in Translation & Summarization.

Finally, you'll build systems that answer questions over your own documents with Question Answering & RAG, and learn how to measure quality, catch bias, and ship responsibly in Evaluation, Bias & Deployment.

No prior NLP experience is required, but basic comfort with Python and a general understanding of machine learning will help. This course walks through the field in order, so each module builds directly on the last.

Content

13 modules

Introduction to NLP

What NLP is, why human language is hard for machines, a short history from rule-based systems to neural models, and the tasks that power search, assistants, and translation.

NLP Basics History of NLP Common Tasks

Text Preprocessing

Cleaning raw text and breaking it into usable pieces: tokenization, lowercasing, stop words, stemming, lemmatization, and regular expressions.

Tokenization Lemmatization Regex NLTK spaCy

Text Representation

Turning text into numbers a model can use with bag-of-words, TF-IDF, and one-hot encoding, plus the limits of sparse representations.

Bag of Words TF-IDF Scikit-learn

Language Models & N-grams

Predicting the next word with n-gram models: probabilities, smoothing, and perplexity, the ideas that every modern language model still builds on.

N-grams Smoothing Perplexity

Word Embeddings

Representing meaning as dense vectors with Word2Vec, GloVe, and FastText, and using similarity and analogies to explore how words relate.

Word2Vec GloVe Cosine Similarity

Text Classification

Building classifiers for spam detection, sentiment analysis, and topic labeling with Naive Bayes, logistic regression, and simple neural models.

Sentiment Analysis Naive Bayes Spam Detection

Sequence Labeling

Assigning a label to every token: part-of-speech tagging, named entity recognition, and the BIO tagging scheme, with Hidden Markov Models and CRFs.

POS Tagging NER CRFs

RNNs & LSTMs

Modeling text as a sequence with recurrent networks, why vanilla RNNs forget, and how LSTMs and GRUs fix it. Includes building a text model in PyTorch.

RNN LSTM GRU PyTorch

Attention & Transformers

Self-attention, multi-head attention, positional encoding, and the encoder-decoder design that replaced recurrence and now drives modern NLP.

Attention Transformers Positional Encoding

Pretrained Models & Fine-Tuning

Transfer learning with BERT, GPT-style models, and the Hugging Face ecosystem: subword tokenizers, fine-tuning on your own data, and choosing the right model.

BERT Hugging Face Fine-Tuning Transfer Learning

Translation & Summarization

Sequence-to-sequence tasks: machine translation, extractive and abstractive summarization, and decoding strategies like beam search and sampling.

Seq2Seq Machine Translation Summarization Beam Search

Question Answering & RAG

Extractive question answering, semantic search with embeddings, and retrieval-augmented generation for building assistants that answer from your own documents.

Question Answering Semantic Search RAG Vector Databases

Evaluation, Bias & Deployment

Measuring NLP quality with F1, BLEU, and ROUGE, spotting bias and hallucinations, and packaging a model as an API ready for real users.

F1 / BLEU / ROUGE Bias Hallucinations Deployment

What You Can Do After This Course

By the end of the curriculum you'll have shipped five real projects and covered the full path from raw text to a deployed language application. Concretely, you'll be able to:

Prepare and represent text — clean, tokenize, and convert raw text into features using TF-IDF and embeddings.

Explain how language models work — from n-grams and word vectors to attention and the transformer architecture.

Build text classifiers — sentiment analysis, spam detection, and topic labeling with classical and neural methods.

Extract information from text — part-of-speech tagging and named entity recognition on real documents.

Fine-tune pretrained models — adapt BERT and other Hugging Face models to your own data and tasks.

Generate and transform text — build translation and summarization pipelines and control how models decode.

Answer questions over your own data — design semantic search and retrieval-augmented generation systems.

Evaluate and ship responsibly — measure quality, catch bias and hallucinations, and deploy a model behind an API.

Show proof of skill — a spam classifier, a sentiment analysis model, a named entity recognizer, a text summarizer, and a RAG-powered document chatbot.