Intro to Natural Language Processing (NLP) in Python for AI

Learn how data quality drives NLP model accuracy and how garbage in, garbage out motivates rigorous cleaning, noise removal, and preprocessing for machine learning.

Set up the NLP environment using a dedicated conda virtual environment, install required packages with pip, download a spacy model, and register a jupyter kernel for notebook work.

Learn how lowercasing standardizes text data in Python using the lower method, while noting capitalization can carry meaning for some projects in NLP and ML workflows.

Remove stopwords with Python and NLTK to simplify text and improve analysis using English stopwords. Customize list by adding or removing words and see how filtering changes a sentence.

Learn to use regular expressions in Python to search, filter, and replace text with defined patterns, including raw strings, start and end anchors, and punctuation removal.

Tokenization breaks text into tokens, such as words or sentences, enabling analysis. Use NLTK's word_tokenize and sent_tokenize, and convert to lowercase to improve consistency.

Standardize text through stemming in pre-processing, using the Porter stemmer from NLTK to reduce words to base forms, lowering vocabulary size for machine learning.

Leverage lemmatization to map tokens to meaningful base forms using WordNet with the NLTK lemmatizer, preserving meaning and real words (learners→learner) unlike stemming, which reduces tokens.

Explore n-grams, including unigrams, bigrams, and trigrams, to analyze word relationships using NLTK, pandas, and matplotlib to tokenize, count frequencies, and visualize results.

Import the Nltc package, load TripAdvisor hotel reviews dataset, and preprocess text with lowercasing, stopword removal, punctuation handling, and tokenization while applying stemming and lemmatization to extract unigrams and bigrams.

Extend your text analysis by tagging tokens with parts of speech and named entity recognition. Explore how these tagging methods reveal insights, generate features for learning, and support Python workflows.

Learn to perform part of speech tagging in Python with spacy and pandas, load the en_core_web_sm model, tokenize text, and analyze parts of speech tags and top nouns.

Learn how named entity recognition identifies and labels people, places, organizations, dates, and quantities in text, using rules and machine learning with spaCy in Python.

Explore sentiment analysis in NLP by defining positive, negative, and neutral sentiment and examining how rule-based methods compute emotional tone in text, such as tweets and product reviews.

Explore rule-based sentiment analysis using lexicon scores to assign polarity, compare TextBlob and Vader, and interpret sentence polarity and compound scores while noting limitations with sarcasm.

Explore transformer models that capture context for sentiment analysis using the transformers pipeline with pre-trained models. Learn how swapping models and handling emoji limitations can change predictions and improve accuracy.

Load a dataset of book reviews with pandas, clean the text, and apply Vader to obtain compound scores and sentiment labels. Then compare results with a transformer pipeline's predictions.

Vectorize text for machine learning by building a bag of words model and tf-idf, explaining their strengths and limitations, and preparing data for NLP algorithms.

Explore the bag of words model with CountVectorizer to convert text into a token-count matrix, reveal vocabulary, and apply binary mode while noting that it ignores word order.

Explore how tf-idf vectorizes text to preserve context beyond the bag of words, using term frequency and inverse document frequency across documents to weight words.

Explore topic modeling, an unsupervised approach that scans documents to identify key patterns and group texts into topics using algorithms like latent Dirichlet allocation and latent semantic analysis.

Explore topic modeling to group news articles and research papers by topics, speeding up manual work, and uncover key themes in customer feedback and social listening.

Learn latent Dirichlet allocation, an iterative topic modeling method that assigns words to topics using a Dirichlet prior and refines assignments toward a steady state.

Apply latent dirichlet allocation in Python with gensim to uncover topics in news articles. Clean and tokenize text, build a dictionary and corpus, then train and interpret the LDA model.

Explore latent semantic analysis, grounded in distributional hypothesis and singular value decomposition, to convert text into document-term and document-topic matrices using SVD for dimensionality reduction, enabling clustering and similarity assessment.

Explore latent semantic analysis in Python using gensim's LSI model, building a document term matrix, specifying two topics, and printing topics to compare with LDA results.

learn how to determine the optimal number of topics using coherence scores, iterating a topic range, plotting results with plt, and balancing coherence with business needs to pick three topics.

Develop a custom text classifier using supervised learning with your own labels, applying logistic regression, naive bayes, and linear support vector machine to text data for natural language processing.

Learn to build a text classifier using logistic regression as a baseline, transforming sentences with a countvectorizer bag-of-words, training on sentiment data, and evaluating with accuracy and a classification report.

Explore Naive Bayes, a simple probabilistic classifier using word clues to separate positive from negative reviews, train a Multinomial NB model in scikit-learn, and measure accuracy.

Learn to implement a linear support vector machine for sentiment analysis, training with SGDClassifier from scikit-learn to separate positive and negative sentences using a linear boundary and evaluating accuracy.

explore and clean data, classify fake vs factual news, plot outputs, and communicate findings to stakeholders using a practical nlp project in python.

Explore how to tag and compare fake and factual news using spaCy, creating separate datasets, extracting token and POS tags, and analyzing top nouns to reveal stylistic differences.

Identify named entities before pre-processing and extract top entities for fake and factual data. Visualize with Seaborn bar plots using a shared color palette to compare fake and factual entities.

Preprocess text data by cleaning, normalizing, and tokenizing: remove leading location tags with regex, lowercase, strip punctuation and stopwords, then tokenize and lemmatize to reveal meaningful unigrams and bigrams.

Explores whether sentiment differs between fake and factual news using Vader sentiment scores in Python for NLP, classifies sentiment as positive, negative, or neutral, and visualizes the results.

Vectorize fake news text, build a dictionary and bag-of-words, and apply LDA to uncover topics, evaluate coherence across topic counts, and identify seven-key topics.

Explore tf-idf vectorization and latent semantic analysis to reveal topics in fake news, using coherence scores to choose the optimal topic count and interpret themes like Trump, Clinton, and others.

Create a custom classifier that differentiates fake and factual news using a bag-of-words vectorization with logistic regression, compare to an SGD-based SVM, and validate high accuracy.

Explore deep learning foundations, with input, hidden, and output layers; weights and activation functions; and training to improve predictions, including CNNs for images and RNNs for language in NLP.

Explore how deep learning powers NLP with transformer models like ChatGPT and large language models trained on vast data to understand and generate human-like text for chat bots and translation.

Discover non-English natural language processing by adapting preprocessing and language models for languages like Hindi and Tamil, using the I and K package, and explore global inclusivity.

NLP will evolve with deeper contextual understanding and reasoning, while multimodal data from images, videos, and audio enhances language models. Ethical considerations, fairness, and privacy guide faster, real-time solutions.