Natural Language Processing [ Building Real World Projects]

Explore tokenization in natural language processing, splitting text into tokens, types, words, and sentences, and learn to organize text by paragraphs while noting common tokenization challenges.

Install and set up the natural language toolkit using Python, download Analytica, and configure the development environment across macOS, Windows, and Linux.

Learn tokenization in natural language processing using Python, splitting text into sentences and words, printing tokens, and iterating through them to illustrate preprocessing.

Explore normalization as a preprocessing step that converts tokenized words into a uniform form to boost information retrieval, while understanding morphology, morphemes, stems, affixes, and stemming.

Explore normalization in natural language processing by applying stemming and memorization with Analytica, using examples like walk and walking, illegal and legal, and a brief look at part-of-speech tagging.

Learn how part of speech tagging labels every word in a sentence as a preprocessing step in natural language processing, using tokenization and Python examples to reveal pos tags.

Learn how part of speech tagging serves as preprocessing in natural language processing, using tokenization and labeling to analyze text, including a movie script, with noted limitations.

Identify stopwords as noise in natural language processing and remove them from tokenized text. Use a stopwords set of common English words to reduce processing and support named entity recognition.

Identify names in text and classify them as entities such as person, organization, or GPE to enable indexing, information extraction, and Q&A systems.

Learn how named entity recognition uses analytic to tokenize text, tag entities, and identify people and places, while noting misidentifications and the option to output just the named entities.

Explore text classification and the naive algorithm with real-world examples like spam filtering, sentiment analysis of movie reviews, topic and language identification, and authorship attribution.

learn to build a text classifier for movie reviews using naive bayes, with preprocessing steps like loading the Analytica corpus, labeling reviews as negative or positive, and tokenizing words.

Build a text classifier by selecting the top 3000 most common words as features. Use a frequency distribution approach to filter noise and train on positive and negative reviews.

train a naive bayes classifier with a 75/25 split and a 3000-word feature set, then measure accuracy around 82% and identify top informative features like outstanding and idiotic.

Explore improving the classifier in natural language processing by adjusting train-test splits, varying the number of most common words, applying normalization, and removing punctuation to beat 82 percent accuracy.

demonstrate building a real-world sentiment analysis app using Twitter data, showing positive and negative cues, and using a 5000-tweet csv corpus to train the model for analytics.

Create a Twitter application to obtain keys and tokens, submit for approval (up to 24 hours), and configure a Python Twitter client to verify credentials and fetch tweets by keyword.

Learn to build a test set for a Twitter sentiment analyzer by fetching 100 recent tweets for a keyword using the Twitter API, with exception handling and text extraction.

Develop a training set for real-world natural language processing by extracting tweet texts from a 5000-tweet corpus and pairing them with sentiment labels.

Learn to preprocess text for sentiment analysis by building a Python class that lowercases, removes punctuation, links, and hashtags, replaces mentions and usernames, and tokenizes with regular expressions.

Build a real-world tweet sentiment classifier by constructing a vocabulary of words, extracting and applying features, training a classifier, and evaluating overall positive or negative sentiment with percentages.

Build and test a sentiment analysis model by collecting 100 tweets per keyword, preprocessing text, building a vocabulary, extracting features, and classifying tweets as positive or negative.

Explore how Python treats variables as boxes. Assign values (x = 2, y = 8), perform simple operations (5+7, 4-6), and output results, illustrating dynamic value changes.

Explore Python variables and string indexing in practice, including concatenation, zero-based indices, negative indexing, and handling index out of range errors.

Develop Python basics by exploring variables and strings, practicing zero-based indexing and end-exclusive slicing on a Facebook name, and using a function to obtain string length.

Explore Python lists for beginners by creating lists with mixed types (integers and strings), using variables and print statements to demonstrate basic list behavior, contrasting with Java.

Explore Python lists by creating a mixed list of numbers and strings, then append and insert values at specific positions. Learn zero-based indexing and removal operations to manage end-of-list updates.

Learn Python list operations, including indexing, popping the first element, removing items, and computing min, max, mean, and sum, plus sorting to manage and analyze list data.