Music Recommendation Backend with Spring Boot and Neo4j

Free download скачать Music Recommendation Backend with Spring Boot and Neo4j
Published 12/2023
Created by Marshall Takudzwa Chabanga
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 41 Lectures ( 19h 23m ) | Size: 12.8 GB

Learn how to build a Music Recommendation Backend with Spring Boot, Neo4j, Spring Cloud, and Collaborative Filtering
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.
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.
Requirements
Spring Framework Basics: Familiarity with the basics of the Spring framework, including inversion of control (IoC), dependency injection, and the concept of beans.
RESTful Web Services: Understanding of RESTful web services and API design principles will be beneficial for building the backend endpoints of the music application.
Database Fundamentals: Basic knowledge of databases and SQL is recommended. While the course focuses on Neo4j, having a general understanding of database concepts will be advantageous.
Git Version Control: Familiarity with Git for version control is important, as it will be used to manage the source code of the project throughout the course.
Microservices Architecture (Optional): Knowledge of microservices architecture concepts would be helpful, as the course incorporates Spring Cloud for building a scalable and resilient backend.
Basic Understanding of Authentication and Authorization: A basic understanding of authentication and authorization concepts will be beneficial, especially when implementing user security with Keycloak.
Maven or Gradle Build Tools: Understanding the basics of either Maven or Gradle build tools will be useful for managing dependencies and building the project.
Java Programming: Proficiency in Java programming is essential, as the course heavily utilizes the Spring Boot framework, which is built on Java.
Description
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!
Who this course is for
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.
Homepage

Код:

https://www.udemy.com/course/music-recommendation-backend-with-spring-boot-and-neo4j/

Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me

No Password - Links are Interchangeable

www.prizrak.ws

Меню навигации

Пользовательские ссылки

Информация о пользователе

Music Recommendation Backend with Spring Boot and Neo4j

Сообщений 1 страница 1 из 1

Поделиться12023-12-18 15:50:50