Skills you will learn

  • AI Assisted Coding
  • GitHub Copilot
  • Amazon Q Developer
  • Windsurf
  • Tool Selection
  • Workflow Optimization
  • Safe AI Usage

Who should learn

  • Students
  • Software Developers
  • Frontend Engineers
  • Cloud Developers
  • Backend Developers
  • Graduates

What you will learn

  • AI Assisted Development Course with Certificate

    • Lesson 01: Course Introduction

      02:53
      • 1.01 Course Introduction AI Assisted Coding for Developers
        02:53
    • Lesson 02: Learning Objectives

      00:59
      • 2.01 Learning Objectives
        00:59
    • Lesson 03: Introduction to AI Assisted Coding

      20:18
      • 3.01 Basics of AI Assisted Coding
        02:24
      • 3.02 Prompting Fundamentals for Developers
        04:48
      • 3.03 Prompting with GitHub Copilot Inline and Chat Interfaces
        03:13
      • 3.04 Demo Prompting for Developers Using Good and Bad Prompts
        09:53
    • Lesson 04: GitHub Copilot for Everyday Coding

      41:56
      • 4.01 Introduction to Github Copilot​
        02:58
      • 4.02 Installing and Initializing Github Copilot
        04:33
      • 4.03 Generating Code with GitHub Copilot Inline and Chat Workflows
        04:23
      • 4.04 GitHub Copilot Hybrid Workflow Combining Inline and Chat with Key Considerations
        05:54
      • 4.05 Refactoring and Debugging with Copilot​
        02:27
      • 4.06 Demo Design the First Component with GitHub Copilot
        13:45
      • 4.07 Demo Debugging with GitHub Copilot
        07:56
    • Lesson 05: Amazon Q Developer for Cloud Coding

      27:39
      • 5.01 Introduction to AWS Amazon Q Developer​
        02:30
      • 5.02 Installing and Initializing Amazon Q Developer​
        02:19
      • 5.03 Code Creation Workflows in Amazon Q Developer
        01:11
      • 5.04 Hybrid Workflow in Amazon Q Developer
        01:41
      • 5.05 Refactoring and Debugging with Amazon Q Developer
        02:10
      • 5.06 Demo Generating a Secure S3 Upload Handler with Amazon Q in VS Code
        09:09
      • 5.07 Demo Debugging and Refactoring with Amazon Q Developer
        08:39
    • Lesson 06: Windsurf for Agentic AI Coding

      24:16
      • 6.01 Introduction to Windsurf​
        02:46
      • 6.02 Installing and Initializing Windsurf​
        01:59
      • 6.03 Code Generation with Windsurf​
        02:12
      • 6.04 Refactoring and Debugging Code with Windsurf​
        01:59
      • 6.05 Demo Building a Book Catalog API with Windsurf
        07:47
      • 6.06 Demo Debugging a Broken Expense Tracker with Windsurf Cascade
        07:33
    • Lesson 07: Cursor for Code Understanding and Refactoring

      30:56
      • 7.01 Introduction to Cursor
        02:28
      • 7.02 Installing and Initializing Cursor
        02:10
      • 7.03 Code Generation with Cursor
        02:47
      • 7.04 Refactoring and Debugging with Cursor
        01:56
      • 7.05 Demo Experiencing Cmd Ctrl K Inline Editing vs Traditional Chat with Cursor
        08:55
      • 7.06 Demo Multi File Context with At References and Composer with Cursor
        12:40
    • Lesson 08: Choosing the Right AI Tool

      12:37
      • 8.01 Strategic Factors in AI Tool Selection
        01:06
      • 8.02 AI Tool Selection Strategy
        02:50
      • 8.03 Demo Choosing the Right AI Tool for Python Development
        08:41
    • Lesson 09: End to End AI Assisted Coding Workflow

      19:18
      • 9.01 AI Assisted Coding Workflow Overview
        03:11
      • 9.02 Translating Requirements into AI Prompts
        01:34
      • 9.03 Coding with AI Assistance
        01:04
      • 9.04 Validating AI Generated Code
        01:22
      • 9.05 Demo Build an API Endpoint for a Customer Database in Cursor
        12:07
    • Lesson 10: Beginner Mistakes Risks and Safe Usage

      14:47
      • 10.01 Avoid Over Dependency via Critical Thinking
        01:44
      • 10.02 Avoid Vulnerabilities with the Security Review Checklist
        02:05
      • 10.03 Safe AI Assisted Coding Playbook
        02:06
      • 10.04 Demo Identifying the Beginner Mistakes Using Code Review Simulation
        08:52
    • Lesson 11: Key Takeaways

      01:15
      • 11.01 Key Takeaways
        01:15
      • Knowledge Check
About the Course

AI coding tools are changing how developers work, but many people have not learned how to use them effectively. In this course, you’ll focus on four key tools: GitHub Copilot, Amazon Q Developer, Windsurf, and Cursor. You’ll learn how to choose the right tool, use them together in a complete workflow, and avoid common beginner mistakes. By the end, you’ll know how to use these tools confidently and effectively.

Topics Covered:

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FAQs

  • Do I need to be an experienced developer to take this course?

    A working knowledge of coding fundamentals will help you get the most out of this course. It is designed for practicing developers rather than complete beginners, though the introductory lessons build up the AI-specific concepts from scratch.

  • Is this course free?

    Yes, completely free. You get full access to every lesson and receive a professional certificate at no cost once you complete the course.

  • What is AI-assisted coding?

    It means using AI-powered tools to help write, complete, review, and refactor code. This lets developers work faster, catch errors sooner, and spend more time on bigger problem-solving tasks. Lesson 03 explains how it works in detail.

  • Which AI coding tool does this course recommend?

    The course does not recommend just one tool. Instead, Lesson 08 gives you a practical way to choose the best tool for your task, environment, and workflow. Each tool has its own strengths for different situations.

  • What makes Windsurf different from tools like GitHub Copilot?

    Windsurf operates as an agentic AI coding tool, meaning it can take multi-step autonomous actions rather than just suggesting the next line of code. Lesson 06 covers what this means in practice and when that kind of tool is the right choice.

  • When should I use Cursor instead of other tools?

    Cursor is particularly strong for understanding and refactoring existing codebases — especially large or complex ones. If you are working in unfamiliar code or need to make structural changes safely, Cursor's codebase-aware features give it a significant edge. Lesson 07 covers this in detail.

  • What is Amazon Q Developer, and who is it for?

    Amazon Q Developer is an AI coding assistant designed for developers working in AWS environments. It provides suggestions based on your cloud setup, making it very helpful for infrastructure and cloud-native development. Lesson 05 covers all the details.

  • What are the biggest risks of using AI coding tools?

    The main risks include overreliance on them, security vulnerabilities in generated code, licensing issues with AI-suggested code, and logic errors that appear correct but behave differently. Lesson 10 explains all these risks and shows you how to avoid them.

  • Can AI-generated code introduce security vulnerabilities?

    Yes, and this is a key point developers should know before depending on AI suggestions. AI tools can repeat insecure patterns from their training data without warning. Lesson 10 explains how to review AI-generated code with security in mind.

  • What does an end-to-end AI-assisted workflow look like?

    It means using AI to support the entire development process, from writing code and exploring a codebase to testing, debugging, and refactoring. This is more than just using AI for autocomplete. Lesson 09 gives a practical walkthrough of this workflow.

  • What does an end-to-end AI-assisted workflow look like?

    It means using AI to support the entire development process, from writing code and exploring a codebase to testing, debugging, and refactoring. This is more than just using AI for autocomplete. Lesson 09 gives a practical walkthrough of this workflow.

  • Will this course cover prompt engineering for coding?

    The course teaches practical prompting techniques for each tool, including how to request better code suggestions and improve your prompts. While it is not a full prompt-engineering course, you will finish with a solid understanding of how to prompt AI coding tools well.

  • Are these tools only useful for individual developers, or can teams use them?

    Both can benefit. Individual developers use them to write and understand code more quickly. Teams can add them to shared workflows, code reviews, and CI/CD pipelines. The course covers how both individuals and teams can use these tools.

  • How long does this course take?

    The course is in-depth and covers extensive hands-on content using several tools. It is completely self-paced, so you can go through it at your own speed, depending on your schedule and experience.

  • Is there a certificate?

    Yes, a free professional certificate is included upon completion, which you can add to your LinkedIn profile or resume straight away.

  • Can I add this certificate to my LinkedIn profile?

    Yes. Once you earn your certificate, you can list it under Licenses and Certifications on LinkedIn — a strong signal to engineering teams and hiring managers that you are actively building the AI-assisted development skills that modern software roles increasingly expect.

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  • Career Impact Results vary based on experience and numerous factors.