Introduction to Natural Language Processing in Python [2024]

Break text into tokens with tokenization, capturing words, phrases, symbols, and punctuation. Support vocabulary creation, text normalization, and feature extraction for NLP tasks such as sentiment analysis and text classification.

Explore tokenization in language processing and how a tokenizer yields tokens from sentences such as the capital of Vietnam is Hanoi, including how hashtags and social media affect tokenization.

Explore how regular expressions power natural language processing tasks by tokenizing text, cleaning data, and matching patterns for name entities, validation, and text generation or replacement.

Implement regular expressions for tokenization using regex and nltk tokenizers, and learn three pattern options: \w+ for word characters, \d+ for digits, and \S+ for non whitespace.

Discover how the Treebank tokenizer splits text into tokens for natural language processing, using punctuation, contractions, hyphenated words, and handling parentheses and brackets as separate tokens for syntactic analysis.

Implement and analyze the Treebank tokenizer and its word tokenizer to show how tokens reveal sentence components and negation semantics.

Explore the tweet tokenizer, a specialized Python NLP toolkit feature designed to tokenize tweets, preserving hashtags, mentions, emoticons like xd, and internet slang for accurate social media analysis.

Implement the tree tokenizer from nltk's tweet tokenizer and tokenize sample tweets, including hashtags. Tune reduced length, strip handles, and preserve underscore case to control normalization.

Explore how word normalization standardizes variations in forms, improving NLP preprocessing. Apply techniques like lowercasing, stemming, lemmatization, stopword removal, and spell correction for consistent text analysis.

Explore stemming, an NLP technique that reduces words to their base form by removing prefixes and suffixes, enabling efficient processing and retrieval with the Porter stemmer and Snowball stemmer.

Implement stemming with the Snowball and Porter stemmers across languages, handling unicode data and stop words to demonstrate results on Arabic, Danish, Dutch, German, French, Hungarian, and Italian.

Learn lemmatization, a word normalization technique that maps words to their base or dictionary form using a lexicon, outperforming stemming for accurate language analysis in NLP tasks.

Learn how the WordNet lemmatizer in Python's NLTK uses the WordNet lexicon to convert words to base forms, leveraging synsets, synonyms, antonyms, and semantic relationships.

Learn to implement the WordNet lemmatizer in Python using NLTK, download required corpora, tokenize text, assign part-of-speech tags, and compare lemmatization with stemming.

Explore the spaCy lemmatizer in Python, learn to convert words to their lemmas using a pretrained language model, and implement end-to-end from installation to token lemmatization in Colab.

Explore stopword removal in natural language processing, a text preprocessing step that reduces dimensionality by removing common words, with a Python NLTK workflow for tokenization and lowercasing.

Case folding converts text to lowercase as a text normalization step, reducing dimensionality and improving text consistency for NLP tasks like classification, retrieval, and sentiment analysis.

Explore how n-grams capture local context in text by sliding a window of size n, with examples of unigrams, bigrams, and trigrams, and their use in NLP tasks.

Explore word2vec and word embeddings, including continuous bag of words and skip-gram models, to learn dense vectors that capture semantic relationships from text through unsupervised distributed representation.

Explore the skip-gram method, a word embedding technique that learns context-based word representations with a shallow neural network, enabling efficient NLP tasks like sentiment analysis and translation.

Implement Word2Vec in Python using Google Colab, download and read the BBC News dataset of 2225 articles, and tokenize with preprocessing like lowercasing and punctuation removal.

Explore word2vec implementation part 2 by building a tokenizer in TensorFlow, configuring vocabulary size, filters, and lowercase handling, and converting text to sequences.

Learn skip-gram implementation in python by converting text to sequences with a tokenizer, generating word IDs, and training with a defined window size and negative sampling.

continue with skip-gram implementation part 2 by building inputs and labels, tokenizing words, and applying unigram distribution negative sampling with a tf random uniform candidate sampler to train the model.

Develop a skip-gram data generator that produces positive and negative context and target word pairs using a window size, vocabulary size, and a sampling table.

Implement the skip-gram model in TensorFlow using the Keras functional API with inputs for context and target, tuning batch size, embedding size, window, and negative samples to train word vectors.

Explore building a skip-gram model in tensorflow: create context and target embeddings, compute dot products, assemble a tf.keras model, compile with binary cross-entropy, and evaluate vector similarity with cosine distance.

Implement a validation class for the skip-gram model, wiring embedding layers, tokenizer, and cosine similarity to compute top-k similar words during end-of-epoch evaluation.

Implement the training loop for skip-gram in a Python NLP course, defining a data generator and a validation callback, and train the model over multiple epochs.

Explore the bag-of-words model for text representation in natural language processing. Learn tokenization, vocabulary creation, and vectorization to convert documents into frequency-based vectors, where word order is not considered.

Implement the bag of words algorithm in Python by preprocessing text with NLTK, removing stopwords, applying stemming or lemmatization, building a vocabulary, and constructing the bow matrix.

Explore the three main data types: structural, semi-structured, and unstructured, with examples ranging from tabular Excel/CSV to text, image, audio, and video data.

Practice text cleaning and tokenization in Python using NLTK and TextBlob, exploring regex-based cleanup, sentence tokenization, and n-grams like bigrams and trigrams.

Tokenize text with Keras and TextBlob, using preprocessing to convert text into sequences. Explore tweet tokenizer, multi-word expression tokenizer, regular expression tokenizer, whitespace tokenizer, and word tokenizer, noting tokenization challenges.

Explore singularizing and pluralizing words and language translation using TextBlob, including extracting words, pluralizing forms, and translating Spanish to English within simple text workflows.

Transform text via feature extraction into numerical features for NLP, enabling text classification and sentiment analysis with methods like bag of words, tf-idf, and embeddings.

Learn to perform feature extraction in natural language processing with Python by using pandas and TextBlob to count words, detect wh words, and extract sentiment polarity.

Implement subjectivity and language feature extraction in Python using TextBlob on DataFrame rows, detect languages with language-detect, and handle errors.

Explore Zipf's law in natural language processing, where word frequency is inversely proportional to rank in a large corpus; few words dominate, many remain infrequent.

Implement Zipf's law in Python using NLTK, tokenize text, remove stopwords, compute token frequencies from the 20 newsgroups dataset, and plot actual versus expected frequencies in a log-log plot.

Explore tf-idf, a statistical measure combining term frequency and inverse document frequency to evaluate a word's importance in a document relative to a corpus, enabling feature extraction and text mining.

Learn to implement tf-idf using sklearn's TfidfVectorizer on a text corpus, convert documents to a dense matrix, and create a pandas dataframe with top features and vocabulary.

Master feature engineering by transforming, selecting, and combining data to boost machine learning performance, using techniques like scaling, normalization, one-hot encoding, PCA (principal component analysis), aggregation, and imputation.

Practice feature engineering in Python by implementing Jaccard and cosine similarity on text pairs, using nltk tokenization and lemmatization, tf-idf vectorization, and cosine similarity metrics.

Create word clouds to visualize text data and highlight frequent terms in nlp. Implement this technique using Python tools to support exploratory data analysis and customizable visuals.

Explore spaCy, a fast, production ready nlp library in Python that enables tokenization, part-of-speech tagging, named entity recognition, dependency parsing, and customizable pipelines.

Apply spaCy tokenization on Unicode text by loading the English model, creating a doc object, and iterating tokens; customize the tokenizer with domain-specific special cases.

Explore tokenization in Python with spaCy, focusing on custom punctuation rules and special-case precedence. Use spaCy's explain tool to debug tokenization and inspect rule patterns.

Explore sentence segmentation with spaCy, learning how to identify sentence boundaries using a dependency-based approach, iterate over sentence spans in a doc, and prepare text for downstream tasks.

Learn lemmatization with spaCy by applying a model to convert tokens into lemmas and create rules to distinguish official city names from nicknames for the booking API.

Explore machine learning, a branch of AI that enables computers to learn from data, train models, and improve predictions across supervised, unsupervised, semi-supervised, and reinforcement learning.

Explore hierarchical clustering, including agglomerative and divisive approaches, how dendrograms visualize cluster hierarchies, and how distance metrics and linkage criteria influence results.

Implement hierarchical clustering on text data using tf-idf features and cosine similarity, with preprocessing steps like tokenization, stop-word removal, and WordNet lemmatization on the 20 newsgroups dataset.

Learn to implement hierarchical clustering on tf-idf features, convert to a dataframe, compute cosine distance, build a linkage matrix, and extract four clusters for analysis.

Explore k-means clustering, an unsupervised algorithm that partitions data into a predetermined number of clusters by assigning points to the nearest centroid and updating centroids to minimize within-cluster variance.

implement four-cluster k-means on tf-idf representations of news articles, using scikit-learn to fit and predict clusters, and apply the elbow method to determine the optimal k

Explore supervised learning, where labeled data trains models to map input features to output labels. Learn data collection, preprocessing, model selection, training, evaluation, and prediction with metrics and applications.

Classification categorizes data into three defined classes using input features and predicts the class label of new instances, a core supervised learning technique used in spam detection and sentiment analysis.

Logistic regression models the probability of a binary outcome using the sigmoid function on a linear combination of input features, with coefficients learned by optimization and a 0.5 threshold.

Explore Naive Bayes classifiers, a family of probabilistic machine learning algorithms for classification based on Bayes theorem. Understand the independence assumption and posterior-based predictions, with Gaussian, Multinomial, and Bernoulli variants.

Learn k-nearest neighbors (KNN), a nonparametric, instance-based algorithm for classification and regression that uses distance measures to predict from the k nearest neighbors.

Learn to implement text classification in Python by preprocessing reviews, building a tf-idf representation, and evaluating logistic regression, Gaussian Naive Bayes, and k-NN models with cross-tab comparisons.

Explore regression as a method to model relationships between dependent and independent variables. Distinguish linear and nonlinear forms—polynomial, exponential, and logistic regression—and evaluate with MSE, R-squared, RMSE.

Explore and implement a linear regression model with sklearn, using tfidf features to predict review scores and interpret coefficients, intercept, and predictions on a data frame.

Explore decision tree methods for regression and classification, including random forest and gradient boosting, and learn how tree-based models partition data, ensemble predictions, and balance interpretability with overfitting and instability.

Explore random forest, a versatile ensemble learning method for classification and regression, built from multiple decision trees trained on bootstrapped samples with feature randomization, then aggregated.

Explore gbm and xgboost, where gbm builds a model by sequentially combining weak learners to fit residuals, while xgboost adds regularization, parallelization, pruning, and missing-value handling for scalable, high-performance predictions.

Implement tree methods in natural language processing by lemmatizing text with WordNet and building tf-idf features. Train and compare decision trees, random forests, and XGBoost, with crosstabs and confusion matrices.

Learn how sampling selects data subsets for training, validation, testing, and pre-processing, including random, stratified, undersampling, oversampling, and cross-validation.

Demonstrate sampling on the retail dataset by loading an Excel file and extracting a 10% random sample, then a stratified 2% sample after filtering for United Kingdom, Germany, and France.

Identify and remove highly correlated features using a correlation matrix and a chosen threshold to reduce multicollinearity, then apply strategies like feature removal or PCA and assess model performance.

Techniques to remove highly correlated features using a tf-idf matrix built from the 20 newsgroups data, employing stopwords, wordnet lemmatization, and a correlation heatmap to prune features.

Dimensionality reduction reduces number of input features using feature selection and feature extraction, with PCA and t-SNE, to improve model performance and interpretability while risking information loss and added complexity.

learn how to implement dimensionality reduction with pca on tf-idf features, transform data to two principal components, and visualize categories with a color-coded scatter plot.

Evaluate model performance with metrics such as accuracy, precision, recall, F1, ROC AUC, MSE, and R-squared; use train-test splits, cross-validation, learning curves, and confusion matrices to compare models.

Compute the rmse and mape with sklearn by importing mean_squared_error and mean_absolute_error, calculating rmse as the square root of mse, and converting the mean absolute error to a percentage.

Keep practicing to master deep learning and become a successful deep learning engineer. Download datasets from Kaggle and UCI to develop and share your model.