Install the Angular CLI with npm, set up node, download the latest Windows installer from npm.org, and verify Angular is installed; note we use TypeScript in Angular, not JavaScript.

Create and set up the Angular recommender project by navigating into an empty folder, naming the app, and running ng serve to test locally in a browser.

Create a login page and a dashboard component, wire routing, and apply bootstrap styling to display user items and recommendations, laying groundwork for Firebase integration and the recommender API.

Set up a sql connection named recommender on localhost and the given port, install the server, start it, and verify the connection is running to execute statements.

Explore the fundamentals of databases, including tables, rows, columns, data types, primary keys, and foreign keys, and see how relational and non-relational databases structure and relate data for efficient recommendations.

Add data to the recommender system by populating users, recipes, and ratings tables to enable collaborative filtering and generate personalized recommendations.

Install python 3.7 on your system. Verify the installation by opening a command prompt and typing python, restart your system if needed, and proceed to the next lecture.

Build a Flask API to serve item recommendations from a collaborative filtering model, connecting to a database and exposing a predictions endpoint that returns JSON for a given user.

Combine the recommender API with an Angular app to display dynamic recommended items on a dashboard, connecting to the recommender service, fetching results via HTTP, and rendering item cards.

Apply changes to return recipe names instead of IDs in a Python-based recommender built with Angular, updating database queries and front-end display for clearer item names.

Style the recommender system interface to enhance navigation and aesthetics. Note this section is optional and adds visuals—login, a dashboard title image, and a recipe image—without adding functionality.

Apply css and images to style the home and recipe components, set a responsive background image, and adjust spacing for a readable, user-friendly recommended recipes interface.

Create a Firebase project to enable user login for personalized recommendations and to track user preferences, then enable email and password sign-in and ensure the web app and dashboard run.

Implement a user login flow with email and password, test sign-up and login, and guard the dashboard behind authentication while integrating with firebase for backend support.

Create and save a new user in Firebase, then update the recommender to insert the user into the database and route personalized predictions for authenticated users.

Create a new user from the browser by assigning a Firebase ID and an internal ID to power the recommender system.

Update the front-end service to call the recommender with a new user’s id, fetch tailored recipe recommendations, and display the results after wiring it to receive them.

Add a frontend rating view for reading recipes and saving user ratings to the database, with a loading spinner and a read recipe button that opens a star-rating pop-up.

Learn how to connect to a database, implement a rate item function in Angular, and save a user's recipe rating by inserting the rating value and recipe name into database.

Configure the rating flow to save user ratings to a Firebase database, seed new users with an initial rating, and update routes to support sign-up and personalized recommendations with Angular.

Learn how angular routing passes parameters through routes and displays dynamic values, such as a username, across pages, enabling parameter-based display and processing in a component.