NLP in Python: Probability Models, Statistics, Text Analysis

Explore how probability theory underpins natural language processing, from word prediction and corpus-based word associations to language model evaluation and token-level patterns.

Explore mean, variance, and standard deviation in NLP, using word frequencies and sentence lengths to analyze text data and inform probability-based models.

Explore three probability distributions used in natural language processing, the binomial, Poisson, and normal distributions, and learn how they model word frequencies, sentence lengths, and rare terms with Python.

Apply statistical inference and hypothesis testing to NLP text data, generalizing patterns and evaluating model reliability. Use t test, chi square, and ANOVA to validate sentiment, topics, and author patterns.

Analyze word frequencies in Alice in Wonderland using pre-processing and tokenization. Compare distributions to Poisson and normal models and test chapter differences with a t-test.

Explore the bag of words model, a simple tokenization based feature that converts text into word frequency vectors and supports text classification and information retrieval in Python with CountVectorizer.

Master tf-idf by combining term frequency and inverse document frequency to weight words by their document significance. Implement in Python with sklearn's TfidfVectorizer for keyword extraction, classification, and information retrieval.

Learn how word embeddings represent words as dense vectors in a continuous space to capture semantic and syntactic relationships, with word2vec, glove, and fasttext, plus Python visualization.

Explore word2vec and its skip-gram and cbow models to generate word embeddings. Learn how they differ in capturing context, efficiency, and word similarities using gensim.

Glove embeddings map words to vectors to capture semantic relationships, enabling word similarity and analogy tasks in NLP, with pre-trained models on large corpora.

Explore language models and their real-world NLP applications, from speech recognition to autocomplete, through hands-on text analysis of Alice's Adventures in Wonderland using tokenization and n-grams.

Explore n-gram models—unigrams, bigrams, and trigrams—building and analyzing text to predict next words and enable autocomplete, spell correction, speech recognition, and machine translation.

Explore smoothing techniques for n-grams to avoid zero probabilities. Learn Laplace smoothing, Good-Turing smoothing, and nested smoothing with a practical bigram example.

Perplexity evaluates how well language models predict the next word, with lower values signaling confidence. Explore interpretability, model comparison, and practical applications, including Laplace smoothing for bigrams and perplexity computation.

Build a trigram language model to predict the next word from two preceding words using NLTK and the Gutenberg Alice in Wonderland corpus; evaluate with perplexity for predictive text.

Explore hidden markov models (HMMs) that connect hidden states to observations via transition, emission, and initial probabilities, and use forward or viterbi algorithms for speech recognition and weather prediction.

Master the forward algorithm for hidden Markov models, a dynamic programming method that efficiently computes the likelihood of observation sequences—applied to weather, customer behavior, and language tasks.

Explore the Viterbi algorithm, a cornerstone of hidden Markov models, and learn how dynamic programming decodes the most probable hidden states sequence from observed events.

Learn how the Baum-Welch algorithm trains hidden Markov models by iteratively applying expectation and maximization to update transition, emission, and initial probabilities, guided by forward alpha and backward beta.

Build a part-of-speech tagger with Hidden Markov models by modeling transition and emission probabilities and applying the Viterbi algorithm to infer tag sequences from the Brown corpus.

Explore context-free grammars, their production rules, non-terminals, terminals, and start symbol, and understand derivation, parse trees, and NLP applications like syntax parsing, machine translation, and speech recognition.

Explore probabilistic context-free grammars (pcfgs) and how probabilities on cfg rules resolve parsing ambiguities. Learn to train pcfgs from corpora and apply them to parsing and translation.

Explore parsing probabilistic context-free grammars (pcfgs) to identify the most likely parse tree using rule probabilities, covering cfg basics and the cyk dynamic-programming approach.

Train and evaluate probabilistic context-free grammars (pcfgs) with a treebank, learning rule probabilities to parse sentences and measure performance using precision, recall, and F1.

Explore Bayesian inference, a method for updating beliefs with prior, likelihood, evidence, and posterior. See how this approach informs medical diagnostics, machine learning, and decision making under uncertainty.

The Naive Bayes classifier uses Bayes theorem for text classification tasks like spam detection, sentiment analysis, and topic labeling, leveraging prior, likelihood, and posterior probabilities under the independence assumption.

Learn Bayesian networks, a probabilistic graphical model for NLP, modeling nodes like words, parts of speech, or named entities with edges and conditional probability distributions for inference.

Explore inference in Bayesian networks for NLP, updating beliefs with observed evidence to compute posterior probabilities for words, POS tags, and named entities, using variable elimination and belief propagation.

Apply Naive Bayes to classify text documents using pre-processing, bag of words features, and evaluation metrics for tasks like sentiment analysis and spam detection.

Learn how to parse sentences with probabilistic context free grammars, build parse trees, handle language ambiguity, and support end-to-end nlp pipelines that integrate parsing with text classification.

Explore the Naive Bayes classifier for scalable text classification, using priors, likelihoods, and posteriors with smoothing. Build, evaluate, and implement in Python for tasks like spam detection and sentiment analysis.

Combine parsing with classification to enrich features using syntactic information like noun phrases, verb phrases, and POS tags, improving sentiment analysis and text classification through tf-idf and naive bayes.

Evaluate a combined NLP parsing and classification model on unseen data using accuracy, precision, recall, F1, and confusion matrix, guiding improvements for real-world tasks like sentiment analysis and topic classification.

Learn to detect sentiment and named entities in text using rule-based and machine learning methods, train models on labeled data, and evaluate through practical product review analysis.

Explore Vader, a valence aware dictionary and sentiment reasoner for rule-based sentiment analysis of short, informal text. Examine how lexicons, negations, and modifiers shape scores and reveal strengths and limitations.

Train machine learning models on labeled reviews to identify sentiment and apply them to unseen text, using algorithms like Naive Bayes, logistic regression, SVM, and deep learning.

Explore named entity recognition with spaCy, extracting people, organizations, locations, and dates from text, visualize with Displacy, and customize pipelines with an entity ruler for healthcare, finance, and e-commerce.

Build an end-to-end sentiment analysis pipeline for product reviews. Preprocess text, apply TF-IDF vectorization, train a Naive Bayes model, and evaluate with accuracy, precision, recall, and F1.

Learn how Latent Dirichlet Allocation (LDA), a probabilistic approach, performs topic modeling to uncover hidden themes in text, representing documents as topic mixtures and topics as word distributions.

Explore conditional random fields for sequence labeling, using features and label dependencies to improve named entity recognition, part of speech tagging, and chunking in text data.

Explore how BERT and transformer models revolutionize natural language processing by using bidirectional context, self-attention, and masked language modeling for tasks like sentiment analysis, named entity recognition, and summarization.

Explore transfer learning in NLP by pre-training and fine-tuning pre-trained models like BERT and GPT to excel at sentiment analysis, NER, and text summarization.

Explore LDA topic modeling and CRF sequence labeling on customer reviews, revealing themes and named entities through unsupervised and supervised NLP methods.