Java Developer Course + AI — online mentor‑led program

Course overview

Description generated based on course syllabus and open data.

Program overview: “Java Developer Course + AI”

The program covers a path from Java basics to advanced topics with an embedded AI component. Training is delivered online in a group with a mentor, homework review, regular feedback, and interim tests.

Java Developer + AI learning structure

• Java for beginners (OOP, syntax, types, collections, generics)
• Git essentials (branches, commits, pull requests)
• Core Java (classes, interfaces, exceptions, file I/O)
• AI Skills: LLM, prompting, test/documentation automation, API integration with AI
• Advanced Java (effective practices, patterns, memory topics at overview level)
• MySQL Essential (CRUD, JOIN, indexes, transactions)
• JDBC & Hibernate (ORM, mapping, repositories)
• Web services (REST, JSON, authentication at overview level)
• Spring (Core, Boot, MVC, configuration)
• Course project (backend service with database and API)
• Practical career skills module (resume, GitHub, technical interview basics)

Who the Java Developer + AI course suits and who it does not

Suits

• Beginners without commercial experience starting from scratch.
• Technical students seeking to systematize knowledge.
• Adjacent roles (QA, analysts, admins) transitioning to Java.
• Developers from other languages moving to Java + Spring.

Not suited

• Those unable to allocate regular practice time and complete homework.
• Those expecting immediate change without working through material.
• Those strictly preferring offline‑only format.

Typical problems → indicative outcomes (Java Developer + AI)

• Lack of a systematic base → builds understanding of OOP, collections, exceptions, and code practices.
• Tooling uncertainty → develops a working Git workflow and IDE proficiency.
• Data layer gaps → develops SQL (JOIN, indexes, transactions) and ORM via Hibernate.
• Backend questions → delivers REST services with Spring Boot and basic configuration.
• AI feels abstract → clarifies LLM, prompting, and API integrations through examples.

Comparison with alternatives for learning Java and AI

• Self‑study: flexible, but higher risk of gaps and no systematic review of solutions.
• Short bootcamps: fast, but limited depth and little time for practice.
• Academic programs: fundamental, but longer timelines and less focus on industry tooling.

Skills and deliverables after completing Java Developer + AI

• Java coding with OOP, collections, generics, and exception handling.
• Working Git process: branching, reviews, pull requests.
• Production‑oriented SQL: data modeling, queries, index‑based optimization.
• Database access via JDBC and ORM mapping with Hibernate.
• REST API development on Spring Boot with basic testing (JUnit, integration approaches).
• Using AI tools for test generation, documentation, and API‑based integrations.
• Course project with a GitHub repository and technical documentation.

Java Developer + AI learning format

• Group online sessions with a mentor; session recordings available for review.
• Homework assignments with review and feedback.
• Interim tests for progress self‑assessment.