Music Recommendation Backend with Spring Boot and Neo4j

Music Recommendation Backend with Spring Boot and Neo4j, available at $19.99, with 41 lectures, and has 32 subscribers.

You will learn about Introduction to Backend Development: Understand the basics of backend development and the role it plays in building complex applications Spring Boot Fundamentals: Dive into the world of Spring Boot and learn how to rapidly develop powerful and scalable backend applications. Neo4j Graph Database: Explore the fundamentals of Neo4j and discover how graph databases can revolutionize data modeling for your music application. Implementing Collaborative Filtering: Learn the principles behind collaborative filtering and how to implement personalized music recommendations for users. Spring Cloud for Microservices: Understand the concepts of microservices architecture and leverage Spring Cloud to build a scalable and resilient backend. Cipher Queries with Neo4j: Master the art of crafting secure and efficient cipher queries to interact with Neo4j and optimize your database operations. User Authentication with Keycloak: Implement secure user authentication and authorization using Keycloak to ensure the privacy and security of your users' data. Real-World Application Development: Apply your knowledge in a hands-on manner by building a fully functional music backend application throughout the course. This course is ideal for individuals who are Developers looking to enhance their backend development skills. or Those interested in exploring the world of graph databases and Neo4j. or Individuals eager to build a real-world music application using modern technologies. or Anyone aiming to understand collaborative filtering for personalized content recommendations. It is particularly useful for Developers looking to enhance their backend development skills. or Those interested in exploring the world of graph databases and Neo4j. or Individuals eager to build a real-world music application using modern technologies. or Anyone aiming to understand collaborative filtering for personalized content recommendations.

Enroll now: Music Recommendation Backend with Spring Boot and Neo4j

Summary

Title: Music Recommendation Backend with Spring Boot and Neo4j

Price: $19.99

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Introduction to Backend Development: Understand the basics of backend development and the role it plays in building complex applications
Spring Boot Fundamentals: Dive into the world of Spring Boot and learn how to rapidly develop powerful and scalable backend applications.
Neo4j Graph Database: Explore the fundamentals of Neo4j and discover how graph databases can revolutionize data modeling for your music application.
Implementing Collaborative Filtering: Learn the principles behind collaborative filtering and how to implement personalized music recommendations for users.
Spring Cloud for Microservices: Understand the concepts of microservices architecture and leverage Spring Cloud to build a scalable and resilient backend.
Cipher Queries with Neo4j: Master the art of crafting secure and efficient cipher queries to interact with Neo4j and optimize your database operations.
User Authentication with Keycloak: Implement secure user authentication and authorization using Keycloak to ensure the privacy and security of your users' data.
Real-World Application Development: Apply your knowledge in a hands-on manner by building a fully functional music backend application throughout the course.

Who Should Attend

Developers looking to enhance their backend development skills.
Those interested in exploring the world of graph databases and Neo4j.
Individuals eager to build a real-world music application using modern technologies.
Anyone aiming to understand collaborative filtering for personalized content recommendations.

Target Audiences

Developers looking to enhance their backend development skills.
Those interested in exploring the world of graph databases and Neo4j.
Individuals eager to build a real-world music application using modern technologies.
Anyone aiming to understand collaborative filtering for personalized content recommendations.

Welcome to “Building a Music Recommendation Backend,” a comprehensive Udemy course that takes you on a journey to create a robust and real-world music application using cutting-edge technologies. This course is designed for intermediate to advanced developers who want to dive into backend application development and explore the power of Spring Boot, Neo4j, Spring Cloud, Collaborative Filtering, Cipher Queries, and Keycloak.

What You’ll Learn:

Introduction to Backend Development: Understand the basics of backend development and the role it plays in building complex applications.
Spring Boot Fundamentals: Dive into the world of Spring Boot and learn how to rapidly develop powerful and scalable backend applications.
Neo4j Graph Database: Explore the fundamentals of Neo4j and discover how graph databases can revolutionize data modeling for your music application.
Implementing Collaborative Filtering: Learn the principles behind collaborative filtering and how to implement personalized music recommendations for users.
Spring Cloud for Microservices: Understand the concepts of microservices architecture and leverage Spring Cloud to build a scalable and resilient backend for your music application.
Cipher Queries with Neo4j: Master the art of crafting secure and efficient cipher queries to interact with Neo4j and optimize your database operations.
User Authentication with Keycloak: Implement secure user authentication and authorization using Keycloak to ensure the privacy and security of your users’ data.
Real-World Application Development: Apply your knowledge in a hands-on manner by building a fully functional music backend application throughout the course.

Who Should Take This Course:

Developers looking to enhance their backend development skills.
Those interested in exploring the world of graph databases and Neo4j.
Individuals eager to build a real-world music application using modern technologies.
Anyone aiming to understand collaborative filtering for personalized content recommendations.

By the end of this course, you’ll have the skills and knowledge needed to create a sophisticated music recommendation backend, and you’ll be well-equipped to tackle similar challenges in real-world application development. Enroll now and embark on your journey to becoming a proficient backend developer!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Project Setup

Lecture 1: Create Microservices – Spring Cloud

Chapter 3: Keycloak Server Setup

Lecture 1: Keycloak Integration Part 1

Lecture 2: Keycloak Mysql Database Connection

Chapter 4: Auth Service

Lecture 1: Create User and Email Verification – Keycloak Integration

Lecture 2: Implement Forgot Password – Keycloak Integration

Chapter 5: User Service

Lecture 1: User Profile Update And Image Upload with BunnyNet Integration

Chapter 6: Music Service – Neo4J Integration

Lecture 1: Create Models and Neo4j Repositories

Lecture 2: Business Layer & Cipher Queries

Lecture 3: REST API – (GenreApi, SongApi, AlbumApi, PlaylistApi)

Lecture 4: Add user PLAYS songs Relationship & Get User Playlist Songs (Projection)

Lecture 5: Get Artist Songs and Album Songs using Neo4JClient

Lecture 6: Update User, Album and Playlist Details using Cipher Query SET

Lecture 7: Update Song Details with Cipher Query SET

Lecture 8: Update Posted Comment and Genre

Lecture 9: Get Paginated Song Comments

Lecture 10: AI/Large Language Model – User Comment Analysis using LangChain and ChatGPT

Lecture 11: Instant Release Song or Album

Lecture 12: Add Spring Security

Lecture 13: Release Scheduled Song or Album

Lecture 14: BunnyNet Video Processing Webhook Status{FNISHED}

Lecture 15: Artist Create Song – Complete BunnyNet Integration

Chapter 7: Music Recommendation

Lecture 1: Artist Followers Recommendation – Collaborative Filtering Cipher Query

Lecture 2: Song Likes Similarity Recommendation Cipher Query Full Explanation

Lecture 3: Artist Interest Recommendation – Sentiment Analysis through LLM

Lecture 4: Song Play History Recommendation Cipher Query

Lecture 5: Recommend latest songs for the Artists you Follows

Lecture 6: Final Recommendation Cipher Query using UNION

Lecture 7: Final Music Recommendation Java Implementation – Powerful Real-World Example

Chapter 8: Messaging Service

Lecture 1: Configure JavaMailSender And Thymeleaf Template Engine

Lecture 2: Implement Send Send Song Comments and bind details to the template

Lecture 3: Add Notification Business Logic and Create Auth Feign Client

Lecture 4: Complete send email and intercept request token

Chapter 9: Task-Scheduler Service

Lecture 1: Task managent (CRUD)

Lecture 2: Implement Task Scheduling

Lecture 3: NFLOW integration and create publish workflow (State Machine)

Lecture 4: Complete Publish Album or Songs

Lecture 5: Feign Client Configuration with Client Credentials Grant Type

Chapter 10: Extra

Lecture 1: Send User Comment Email to an Artist

Lecture 2: Sync music-service User with the user-service User

Lecture 3: Dockerizing microservices

Instructors

Marshall Takudzwa Chabanga
Senior Software Engineer

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