NLP with Python Masterclass: Unlock the Power of Language AI

Explore how natural language processing enables computers to understand and interact with human language, processing large text data with Python libraries to power tasks such as classification, regression, and summarization.

Trace the history of natural language processing from symbolic NLP and the Turing test to statistical NLP and the neural NLP era driven by deep neural networks.

Explore why natural language processing matters today through chatbots and voice assistants like Siri, and see how Alan Turing’s legacy relates to its origins and uses such as WhatsApp bots.

Explore diverse natural language processing applications, including speech recognition, text understanding, text generation, classification, and summarization. Learn about sentiment analysis, recommendations, machine translation, question answering, spelling correction, and chatbots.

Explore how NLP projects differ in data collection, preprocessing, and feature engineering, and compare models from traditional ML to autoencoders and transformers, with Bleu score evaluation and deployment.

Explore how to prepare a text dataset for natural language processing: identify features, select the sentiment target, encode text into numbers, and address word order with recurrent neural networks.

Navigate the NLP project steps from data collection and cleaning to sentence segmentation, tokenization, stop-word removal, stemming and lemmatization, feature extraction, and model evaluation.

Explore essential Python libraries for natural language processing, including NLTK, spaCy, TextBlob, Gensim, Transformers, and Stanford NLP, and learn how regex and Pattern enable vectorization, classification, and regression tasks.

Learn to install and import the re library, then use match, search, and sub to apply regular expressions with raw strings and character classes.

Explore using the regular expressions library for data cleaning, handling non-word characters, digits retention, and space compression by substituting with a null string.

Learn how tokenization in NLP uses the NLTK library to convert text into tokens, using word tokenize and sentence tokenize to handle paragraphs and sentences.

Compare stemming and lemmatization, noting that stemming ignores context and is faster, while lemmatization uses context and part of speech, aided in Python by NLTK stemmers and lemmatizers with WordNet.

Learn data cleaning and preprocessing in natural language processing, including lowercasing, removing punctuation, HTML tags, URLs, emails, spelling corrections, and duplicate removal, plus whitespace normalization and multilingual handling.

Learn the key pre-processing steps for nlp, including converting to lowercase, removing punctuation with string methods and regex, and detecting language with lang detect.

Explore vectorization of text with the bag of words model, converting documents into fixed-length frequency vectors via a growing vocabulary, noting sparsity and the absence of word order.

Explore term frequency and inverse document frequency within the bag of words framework, explaining how term weights are computed and how tf-idf vectorization arises from combining tf and idf.

Explore tf-idf based vectorization to convert text into meaningful document vectors by computing term frequency and inverse document frequency, with smoothing to prevent zero values.

Explore n-gram models in NLP, from one-grams to three-grams, and learn how bag-of-words vectorization creates feature columns for documents.

Explore one-gram, two-gram, and three-gram models with bag-of-words and tf-idf features for document classification tasks like spam filtering, emotion detection, and sentiment classification, based on within-sentence sequences.

Explore the basics of natural language processing with Python using NLTK, including WordNet, lemmatization and stemming, while setting up Colab or Jupyter notebooks and working with n-grams.

Learn how to use the wordnet lemmatizer and porter stemmer in Python to obtain root words. Compare stemming and lemmatization and explore wordnet and sentiwordnet dictionaries with nltk.

Import and use the NLTK stopwords list to preprocess text, retrieving English stopwords with nltk.corpus.stopwords.words('english') and noting language options.

Explore tokenizing text with nltk in python, using nltk.word_tokenize to convert a document into a list of words, and prepare for stopwords removal.

Learn to remove stop words in Python using NLTK by filtering words against the English stopword list, and ensure case-insensitive removal with lowercasing.

Explore n-grams in language modeling by defining one-gram, two-grams, and three-grams as consecutive tokens, with hands-on examples and text preparation in Python.

Explore how two consecutive words form bigrams from tokenized text, showing how stop words affect grams, and building a dictionary of n-grams, illustrated with the example 'I like this book'.

Explore implementing n-grams in Python, from 1-, 2-, and 3-gram patterns to generating text with an n-gram dictionary and starting-position generation using NLTK.

Discover how count vectorizer turns text into numbers using n-grams, from unigrams to bigrams, via sklearn's feature extraction and get_feature_names_out. Build a numeric feature matrix ready for sentiment analysis models.

Apply tf-idf vectorization and compare with countvectorizer. Learn term frequency and inverse document frequency, and see how tf-idf values emphasize important terms with fit and transform.

Explore different tokenization methods in NLP using NLTK, including sentence tokenize and word tokenize, and see how their outputs enable pre-processing and TF-IDF vectorization with sklearn for model building.

Explore stemming and lemmatization with NLTK by tokenizing text, extracting root words, and reconstructing processed sentences to compare outcomes and observe lowercased, normalized language.

Learn to tokenize sentences, remove stop words, and use NLTK to tag parts of speech, then leverage these tags for bag-of-words and tf-idf vectorization.

Explore the Python re library for patterns involving emails and digits, compare match and search for first or all occurrences, and apply anchors and quantifiers like dot plus and caret.

Learn how to substitute text with python's regex using re.sub, exploring pattern-based replacements, case sensitivity with flags, and removing punctuation while preserving words and spaces.

Explore finding all matches with Python's findall method to extract words, digits, and whitespace, apply regex like \d+ for numbers, and use split to identify sentences.

Learn to define an email address pattern by listing allowed characters (letters, digits, symbols), including the at sign and at least three following characters, for pre-processing and vectorization.

Explore how word embeddings convert text into dense vector representations using neural networks, including LSTM, pre-trained vectors, and cosine similarity for measuring word relationships.

Explore how to generate and count n-grams, such as 3-grams and 4-grams, from text by counting token sequences across sentences, with practical implementation in Colab.

Discover how embedding vectors are created from text using bag of words, n-grams, and word2vec. Learn about pre-trained vectors, including Bert models and glow word vectors, and hands-on applications.