Hands On Natural Language Processing (NLP) using Python

Explore Python lists as the first data structure, learning non-homogeneous elements, indexing, printing, and list operations like append, insert, update, delete, and iteration.

Learn Python list and dictionary comprehensions, and generator comprehensions, to create and filter data in a single line, with examples from numbers and joining words into sentences.

Learn regex in Python with the re library, using dot star, dot, and plus to match text patterns, including word characters versus any characters.

Explore shorthand character classes in Python regular expressions, including digits, word and non-word characters, and whitespace; learn to group characters, escape dots, and substitute with re.sub.

Learn to clean and normalize a list of sentences using Python regular expressions for preprocessing in natural language processing, removing non-word characters, digits, and extra spaces.

Install the nltc library in Python and install its dependencies, then import nltc and run nltc.download to fetch components for NLP modeling.

Build a bag of words model from 100 frequent words, encoding each document as a binary vector, then convert to a 2D numpy array for NLP processing.

Load a text dataset for sentiment classification by importing files from the txt_sent_token folder with sklearn's load_files, producing negative (0) and positive (1) classes and the X and Y data.

Persist the dataset by saving X and Y as pickle files to speed up loading on large datasets like 50,000 IMDb reviews.

Transform a simple bag-of-words model into a tf-idf representation using sklearn's TfidfTransformer, building on a pre-built CountVectorizer bag-of-words.

Explore logistic regression as a binary classifier for sentiment analysis, using tf-idf features and a 0.5 threshold to predict positive or negative documents, while learning optimal coefficients.

Import sklearn's logistic regression class, instantiate a classifier, and fit it on text_train to train the model. Preview test-set predictions to measure accuracy in the next video.

Import and load the pickled classifier and tf-idf vectorizer, transform a sample sentence, and predict its sentiment as positive or negative. Next, build a Twitter sentiment bot.

Count positive and negative tweets from predicted sentiments and plot a two-bar chart with matplotlib and numpy, labeling axes and visualizing the results.

Fetch a single Wikipedia article on global warming, preprocess the text, and build a vertovec model using gensim, after installing BeautifulSoup and lxml.

this lecture revisits refining a natural language processing model in python by correctly handling punctuation and symbols, removing stopwords, training word vectors, and evaluating word similarity to improve efficiency.