Natural Language Processing (NLP) with Python and NLTK

Understand the difference between structured and unstructured data. See how structured data is organized and easy to search, while unstructured data like tweets and images requires processing to extract structure.

Read a two-column spam dataset (ham or spam and the message), clean and split text by newlines and separators, then load into a two-column structure for NLP classification.

Explore the dataset to learn its shape, column names, and the distribution of spam and ham labels, including missing values, to guide preprocessing before training a classifier.

Explore how a text data pipeline converts raw text into tokens, cleans data by removing stop words, applies stemming, and builds bag of words representations for spam detection.

Learn how to preprocess text by removing punctuation and stop words, clean data, and apply stemming to reduce word forms, before converting text to numeric features for natural language processing.

Tokenize text by splitting on non-word characters with regular expressions in Python. Define a tokenize function, convert text to lowercase, and store tokens for clean text data.

Remove stop words with Python and NLTK using a custom function and list comprehension to reduce token count, then prepare for stemming.

Explore stemming, which reduces words to their root form to shrink a corpus and aid modeling, while examining over stemming, under stemming, and heuristic errors.

Explore how the Porter stemmer in nltk reduces words to their stems, implement a custom stemming function, and apply it after cleaning, tokenizing, and removing stop words.

Learn lemmatization, which maps words to their canonical dictionary forms to preserve language context, and contrast it with stemming's speed and potential loss of meaning.

Explore applying the WordNet lemmatizer in NLTK to compare lemmatization with stemming and prepare cleaned text data for machine learning.

Explore how to vectorize text by converting raw documents into numerical representations using word counts and document frequency features built from a vocabulary.

Learn to build a document-term matrix with count vectorization in Python and NLTK, including text cleaning, tokenization, and deriving vocabulary and frequencies.

Explore n-gram vectorization in Python with NLTK, building unigram, bigram, and trigram features from cleaned text and documents. Learn how stop-word removal and feature columns structure the dataset.

Learn how tf-idf vectorization weighs words by their importance across a corpus, using term frequency and document frequency to produce a vocabulary-based, sparse feature matrix for documents.

Explore feature engineering in NLP by creating new features from domain knowledge, transforming and normalizing data, and evaluating how features help distinguish spam messages from other texts.

Learn to create text features for NLP, including message length, punctuation usage, and word capitalization, and evaluate their impact on classification in Python with NLTK.

Evaluate two new features—message length and punctuation—for distinguishing spam from ham messages, using histograms, normalization, and feature visualization in a Python notebook, to inform NLP model training with nltk.

Apply power transformations to skewed data using Box-Cox and Tukey's methods, selecting lambda to approximate normal distribution, and use square root and fourth root transforms to improve machine learning performance.

Evaluate a spam detection model using accuracy, precision, and recall. Understand false positives, false negatives, and true negatives, and how business requirements affect precision or recall.

Explore k-fold cross-validation to evaluate model performance on unseen data by dividing the dataset into k subsets, training on k-1 folds, and averaging accuracy across folds for robust estimates.

Explore random forests, an ensemble learning method that builds multiple decision trees, aggregates their predictions by majority voting, and applies to classification and regression tasks.

Build a basic random forest model for text data by cleaning, vectorizing, and encoding word presence as features, then evaluate with cross-validation to achieve high accuracy.

Train a random forest classifier for natural language processing with a holdout test, tune trees and depth, and evaluate precision, recall, and accuracy on a spam detector.