Introduction to Natural Language Processing

Explore sentence segmentation and boundary detection through rule-based and machine learning approaches, addressing abbreviations, punctuation, and multi-language challenges, and leveraging tools like NLTK, spaCy, and BERT for robust sentence boundaries.

Explore basic text representation methods and vectorization, focusing on bag of words and tf-idf, including term frequency, inverse document frequency, and how stopwords, semantics, and word order affect content.

Explore word frequency, stop words, and Zipf's law to guide text analysis, vocabulary building, and classification with tf-idf concepts, term frequency, and inverse document frequency.

Explore language models in natural language processing, comparing rule-based, statistical (n-gram, tf-idf) and neural network approaches (RNN, LSTM, transformers), and their applications from next-word prediction to translation and chatbots.

Transform unstructured data from social media, chats, and web content into structured, trainable data through cleaning, tokenization, and noise removal, then apply NLP with embeddings and transformers.

Examine rule-based, probability-based, and neural network approaches to part-of-speech tagging, covering the eight parts of speech, forty-five Penn Treebank tags, and related ambiguity in syntax.

Explore language models from statistical n-gram models to neural and transformer based approaches, highlighting unigram, bigram, and trigram tokenization, frequency-based probabilities, and smoothing techniques.

Learn how a bigram n-gram model uses unigram and bigram counts from a six-sentence corpus, addresses zero probabilities with add-one, add-alpha, and good-tuning smoothing, and computes perplexity.

Analyze NLP evaluation metrics across text classification, generation, summarization, translation, and question answering using accuracy, precision, recall, F1, confusion matrix, ROC, Bleu score, and perplexity.

Learn how ROUGE score evaluates text-to-text NLP tasks by comparing generated text with reference text using ROUGE-1, ROUGE-2, and ROUGE-L metrics, focusing on recall, precision, and F1.

Learn probabilistic hidden Markov models for parts-of-speech tagging, including transition and emission probabilities, initial states, and the sequence tagging process in natural language processing.

Discover how the maximum entropy method uses logistic regression and learned weights to tag parts of speech with feature vectors and softmax probabilities.

Explore topic modeling with LDA, an unsupervised learning method that clusters words into topics based on word topic and document topic probabilities, using Gibbs sampling with alpha and beta parameters.

Learn how the latent dirichlet allocation (lda) method for topic modeling works by computing document-topic and word-topic probabilities, iteratively updating word assignments to topics as an unsupervised learning process.

Explore text similarity measures in natural language processing by converting sentences into vectors with tf-idf, n-grams, or embeddings, then apply cosine and Jaccard similarity.

Explore distributional semantics through Word2Vec by converting words into multi-dimensional vectors with CBOW and skip-gram, capturing semantics beyond tf-idf.

Explore the skip gram model in word2vec and contrast it with continuous bag of words by predicting context words from a target word within a window size of two.

Apply global vectorization by building a corpus-wide co-occurrence matrix to capture word relationships beyond sentences. Contrast with word2vec and skip-gram, learning vectors from corpus-wide co-occurrence with weights and biases.

Explore fast text to handle out-of-vocabulary words by representing words as character n-grams and combining them with word embeddings in a skip-gram framework.

Assess intrinsic evaluations of text embeddings using word similarity, analogy, categorization, and outlier detection with cosine similarity to verify semantics across word2vec, fasttext, glove, and transformer-based models.

Assess word embeddings via intrinsic and extrinsic evaluation, using sentiment analysis, named entity recognition, and translation, and compare models like word2vec, fasttext, bert, and xlnet.

Explore bert, an encoder-only, bidirectional transformer model that uses self-attention and multi-head attention with position encoding to produce context-aware embeddings for text.

Explore transformer-based sentiment analysis with Hugging Face libraries, compare neural networks to transformers, and learn to fine-tune pre-trained models using attention, tokenization, and deployment workflows.

Explore sequence to sequence models that map multi-dimensional input sequences to output sequences using encoder–decoder frameworks with optional attention, enabling machine translation, text summarization, audio and image to text tasks.

Learn NLP basics from tokenization, POS tagging, stemming, lemmatization, and named entity recognition with Spacy and NLTK. Vectorize text with bag-of-words and tf-idf for sentiment analysis.

Explore deep learning approaches for twitter sentiment analysis, from tf-idf and logistic regression to universal sentence encoder and bert, using lstm and other neural networks with evaluation metrics.