Go For Data Science and Natural Language Processing (Golang)

Go for data science and natural language processing in Golang teaches handling strings, tokenization, part-of-speech tagging, sentiment analysis, collocation, and summarization, plus building nlp-powered web apps with Go packages.

Explore how the Go strings package handles length, upper and lower case, contains, count, split, replace, and join to support data science and NLP tasks.

Apply regular expressions in Go to clean text for NLP by normalizing case, removing noise such as emails and phone numbers, and preparing data for tokenization.

Explore language detection in Go by installing and comparing two packages, using Getline and another library, to identify languages like English and French with confidence scores.

Detect languages in Go using the whatlanggo package, printing the detected language and its confidence. Learn to refine results with blacklist options and multi-language testing.

Go for data science and natural language processing demonstrates tokenization in Golang using regex or an NLP package, choosing treebank or simple tokenizers, and printing the tokenized output.

Explore tokenization in Go for natural language processing using regular expressions and string methods, including word, sentence, and character tokenization and pattern-based splitting.

Explore part of speech tagging in Golang by tokenizing text and annotating tokens with pos labels using a perceptron tagger in a Go project.

Develop a Go function that tokenizes input text, filters tokens by the label N.N., and returns a slice of noun chunks as the result.

Learn how to create a custom verb chunk in Go for NLP by building a simple parser, fetching tokens, applying conditional logic, and assembling results.

Explore how to perform named entity recognition chunks in Golang using custom tagging, chunking, and regex patterns, extracting entities like Jesse and London from text.

Explore named entity recognition in Go using the Go4NLP Prose package, tagging persons, organizations, and locations in text with simple Go code and practical examples.

Explore natural language processing in Go using the Prose package, covering tokenization, part-of-speech tagging, and named entity recognition, with file-based input and practical examples.

Demonstrates keyword extraction in Golang for natural language processing, teaching how to identify the most relevant keywords to summarize topics and enable rapid automated keyword extraction for brand monitoring.

This lecture demonstrates a simple Go-based text summarization workflow using the tl;dr approach, comparing attractive and instructive formats and implementing a graph-based ranking method.

Explore sentiment analysis in Go with a rule-based Vader approach using a lexicon of positive and negative words to label text as positive, negative, or neutral and compute compound score.

Explore sentiment analysis in Go with pre-trained models, contrasting a rule-based approach and model-based methods using imdb data, language options, and word-level sentiment scores.

Create a Go mini project for sentiment analysis, loading a labeled dataset, applying a lexicon-based sentiment analyzer, computing positive, negative, neutral, and compound scores, and storing results for evaluation.

Learn to perform statistics in Golang using standard and external packages, computing mean, min, max, mode, standard deviation, variance, and arithmetic, geometric, and harmonic means.

Explore data analysis in Go using the GoTa package to read CSV data, inspect shapes and types, and perform selection, filtering, pivoting, and basic statistics.

Explore Gonum for Go data science, using its statistics, distributions, sampling, and matrix tools to perform vector and matrix operations, optimization, and basic data analysis.

Prepare data for Go machine learning by cleaning and feature engineering, using Python or Julia for preprocessing, then train and evaluate models with Go libraries on hepatitis C virus dataset.

Go for data science with GoML demonstrates building a GoML machine learning workflow: load hepatitis data, train a logistic regression model for binary classification, make predictions, and save the model.

Learn to evaluate a Go-based machine learning model with a confusion matrix, computing true positives, true negatives, false positives, and false negatives to derive precision, recall, sensitivity, specificity, and accuracy.

Explore text classification in Go using Go4ML approach, building a streaming data pipeline with channels, training an online model, and predicting hardware versus software.

Explore building data web apps in Go using frameworks like Gin, Iris, Fiber, Beego, Revel, Tango, and Buffalo; apply natural language processing to add emotion-aware features and practical tooling.

Learn the basics of GoFiber by building a simple web app, managing dependencies, and defining routes to run a basic API with parameters.

Explore how to handle query params in GoFiber, reading and passing parameters across GET and POST routes, and building APIs that accept and return query data.

Discover how to render HTML with GoFiber using a templating engine and configure views. Connect the engine to your app, load templates from views, and render or test messages end-to-end.

GoFiber form handling demonstrates front-end to back-end data flow with form inputs, names, messages, a submit action, and get or post methods using form values.

Explore form handling with the post method, sending user details from the front end to the back end, receiving and rendering results, and improving security over the get method.

Explore how to upload and receive files with GoFiber, detailing front-end to back-end file transfer, form file handling, post requests, encryption, and saving uploaded files to a directory.

Learn how to save and serve static files with GoFiber, including configuring routes, naming static assets, and testing with localhost to render images and downloads.

Learn to render uploaded images in a Go Fiber app by saving files to a static directory and displaying them on the front end with an image template.

Explore how to integrate Bootstrap with GoFiber by importing Bootstrap assets, configuring static and public folders, and rendering templates with Bootstrap components like a container and a Jumbotron.

Learn to build a simple natural language processing web app using a workflow for sentiment analysis, entity and keyword extraction, with GUI and API options for testing.

Build a sentiment analysis web app in Go (Golang) with a web interface and API, using a user input form to score sentiment and return results.

Build a sentiment analysis web API in Golang, exposing a route that accepts a query parameter and returns sentiment results in JSON.

Enhance a sentiment analysis web app by using bootstrap to beautify the interface, incorporating forms, buttons, and a responsive layout to improve usability and visuals.

learn to generate QR codes in Go by importing a GitHub package, using go get and Go modules, and configuring text data and color options for encoding.