ASP.NET Core Razor Pages + Machine Learning (Project Based)

Explore downloading and browsing static files in an ASP.NET Core Razor Pages project, edit and display diary entries, and use machine learning to classify mood as happy or sad.

Explore building a Razor Pages interface using Materialize CSS to design navigation, inputs, cards, and floating action buttons, with custom CSS and icons for diary entries.

Explore the asp.net core razor pages project, covering prerequisites such as Visual Studio and SQL Server Management Studio, razor pages concepts, and a flow with data models, services, and repositories.

Create an ASP.NET Core Razor Pages web application in Visual Studio, then explore the default files and folders, including index and privacy pages, layout, and assets like scripts and wwwroot.

Locate the navigation bar in the layout view, apply materialize styling, copy the updated navigation, adjust privacy and index links, then test in the browser.

Create a data model by defining properties, including a primary key and a sentiment field, then save and push the model to a database table for future machine learning analysis.

Push a diary entry model to a SQL Server database by configuring a connection string, adding EF Core, creating migrations, and updating the database to create a diary entries table.

Define a diary entry interface and accompanying services in an asp.net core razor pages project, enabling add, get, update, delete, and retrieval by id with sentiment prediction.

Inject the database context to implement the diary entry repository with dependency injection, performing CRUD operations (add, update, delete, get, find) for diary entries, and saving changes.

Learn to register interfaces and their implementations in the startup's configure services method, enabling dependency injection for repositories across Razor pages in an ASP.NET Core project.

Add a diary entry input with client-side validation in an ASP.NET Core Razor Pages project, wiring a diary entry model and enabling required validation for the on this day display.

Implement the OnPost action in the diary page model to validate input, inject the diary repository, save the entry to the database, and redirect to the index page.

Display diary entries from the database by using a shared partial page, injecting a diary repository, and rendering entries with sentiment-based icons and placeholder handling for empty data.

Explore implementing delete functionality for diary entries in an ASP.NET Core Razor Pages app, including a delete form, entry id input, and repository call to remove entries.

Master end-to-end edit and update on a Razor page using OnGet to load data. Save changes via a dependency-injected repository after updating the edit form in the page model.

Learn how to update a diary entry with an OnPost method in Razor Pages, using model state validation, diary entry binding, and a primary key to perform an update.

Discover how AlterNet enables sentiment analysis through a console app, using a dataset, data schema, ml context, data split, model training, evaluation, and deployment.

Build a console application for sentiment analysis using machine learning in ml.net, create a dataset with text and sentiment labels, and define input and output classes for training.

Create and initialize an AML context in a console application, load a dataset from a text file, and perform a train/test split to train a sentiment model.

Build and train a machine learning model by creating a text-to-numeric feature pipeline, selecting input and label columns, using binary classification, and applying an 80/20 train-test split.

Learn how to evaluate the accuracy of a built machine learning model by testing predictions on a 20 percent test split, using binary classification metrics and display the results.

Learn how to save a trained sentiment analysis model for later use in an ASP.NET Core Razor Pages project by persisting the prediction engine and its input/output schema.

Integrate a machine learning model into a Razor Pages project by installing the ml package, placing the model in a data folder, and defining input and output classes for sentiment.

Load a trained sentiment model into the ASP.NET Core Razor Pages app, create a prediction engine with input and output classes, and generate sentiment predictions for user notes.