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AI+ Vibe Coder™

  • Beginner-Friendly Approach: Designed for aspiring creators eager to explore AI-assisted coding with ease and confidence
  • Interactive Learning Journey: Blends core coding concepts, intuitive AI tools, and hands-on practice to build real problem-solving skills
  • Project-Driven Growth: Provides guided exercises and practical projects to help you build, refine, and showcase your AI-powered coding talents
Enroll Now
AI+ Vibe Coder™
Self-Paced Online
USD $ 195.00
Instructor-Led Online
USD $ 1110.00

At a Glance: Course + Exam Overview

Our training approach is human‑centred and outcomes‑driven. We focus on what learners can apply confidently.

Category
AI Development
AI Professional
All Courses
English
Language
Software Developer
Program Name
AI+ Vibe Coder™
Prerequisites
    • Basic Computer Skills: Comfortable with operating systems and files.
    • Mathematics Fundamentals: Understanding of algebra and basic statistics.
    • Logical Thinking: Ability to approach problems step by step.
    • Programming Curiosity: Interest in learning coding from scratch.
    • English Proficiency: Ability to follow technical instructions clearly.
Exam Format
Exam details not available.

What You'll Learn

AI-Assisted Coding

Learn how to write, optimize, and debug code using intelligent AI tools and natural language interfaces.

Machine Learning Fundamentals

Understand core ML concepts to build smarter, data-driven applications that adapt and improve over time.

Generative Development Techniques

Explore how generative AI can automate code generation, testing, and creative problem-solving in real-world projects.

Ethical and Responsible Coding

Gain awareness of responsible AI practices, ensuring transparency, fairness, and safety in AI-powered software solutions.

Certification Modules

Module 1: Introduction to Vibe Coding & AI Tools

  1. 1.1 What is Vibe Coding?
  2. 1.2 Evolution of AI in Software Development – Low Code vs No Code vs Vibe Coding
  3. 1.3 Overview of Common AI Coding Tools by Functionality
  4. 1.4 SDLC for a Vibe Coding Product
  5. 1.5 Hands-on Lab: Familiarizing Learners with Multiple AI Coding Tools
  6. 1.6 Case Studies

Module 2: Prompting for Code – Basics & Best Practices

  1. 2.1 Anatomy of a Good Prompt
  2. 2.2 Prompt Types – Instructive, Descriptive, Iterative
  3. 2.3 Prompting Patterns – Zero-Shot, Few-Shot, Chain-of-Thought
  4. 2.4 Hands-on Lab: Practice Zero-Shot, Few-Shot, and Chain-of-Thought Prompting
  5. 2.5 Use-Case 1: Creating a Python Calculator
  6. 2.6 Use-Case 2: Optimizing AI-generated Code Using Different Prompt Types

Module 3: Debugging & Testing via AI

  1. 3.1 Reviewing and Refining AI-generated Code
  2. 3.2 Prompting for Bug Fixes and Test Coverage
  3. 3.3 Using AI-generated Unit Testing
  4. 3.4 Detecting Hallucinations and Unsafe Code
  5. 3.5 Hands-on Lab: AI-Assisted Debugging and Unit Testing
  6. 3.6 Activity Section

Module 4: Building a Simple Full-Stack App with Prompts

  1. 4.1 Planning the App: Frontend + Backend
  2. 4.2 Using IDEs and Code Generators to Scaffold Code
  3. 4.3 Connecting Components Using Natural Language
  4. 4.4 Deploying and Testing the MVP in Simulated Environment
  5. 4.5 Hands-on Lab: Building and Connecting the Frontend and Backend for Contact Form Submission
  6. 4.6 Hands-on Lab: Building a Standalone Desktop Calculator Application Using Tkinter
  7. 4.7 Hands-on Assignment 1: Task Management System – Full-Stack Development Using Prompts

Module 5: Code Ethics, Security, and AI Limits

  1. 5.1 AI Limitations and Biases
  2. 5.2 Prompt Injection and Mitigation Strategies
  3. 5.3 Data Privacy and Secure Coding
  4. 5.4 Responsible Use of AI in Production
  5. 5.5 Hands-on Lab: Build Awareness of AI Limitations and Responsible Practices

Module 6: Capstone Project – Prompt-Driven App

  1. 6.1 Apply All Learned Skills in a Real-World Project
  2. 6.2 Collaborate and Iterate Using AI Tools
  3. 6.3 Demonstrate End-to-End Development Using Prompts
  4. 6.4 Capstone Project Use Case: AI-Powered To-Do List Application
  5. 6.5 Capstone Project Use Case: AI-Powered Note-Taking Desktop App
  6. 6.6 Assignments

Finish the course and get certified

certificate

Industry Opportunities

Opportunity Image

AI Software Developer

Design and build intelligent applications that leverage machine learning models to automate processes and enhance user experiences.

Opportunity Image

Data-Driven Application Developer

Develop smart apps powered by data analytics and predictive modeling to solve real-world business challenges.

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Automation Solutions Architect

Create AI-based automation pipelines that streamline coding workflows, boost productivity, and reduce manual effort.

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Chief AI Innovation Engineer (CAIIE)

Lead next-generation AI software initiatives, driving digital transformation and intelligent product development across industries.

Frequently Asked Questions

Can I apply what I learn in this course to real-world scenarios immediately?
Yes, this certification provides hands-on experience through real coding challenges and AI-driven projects. You’ll be ready to build intelligent applications and automate workflows from day one.
What makes this course different from other Coding and AI courses?
This certification uniquely combines programming fundamentals with AI-powered development, focusing on real-world automation, generative coding, and intelligent software creation.
What type of projects will I work on?
You’ll work on projects like AI-assisted app development, automated code generation, chatbot creation, and a capstone project building an AI-powered coding assistant or tool.
How is the course structured to ensure I actually learn the skills?
The course blends expert-led lessons, interactive coding labs, and practical projects with real-world applications to ensure hands-on mastery of AI-integrated programming.
How does this course prepare me for the job market?
It equips you with high-demand AI coding skills, real-world project experience, and the technical foundation needed for emerging roles in AI software and automation development.

Prerequisites

  • Basic Computer Skills: Comfortable with operating systems and files.
  • Mathematics Fundamentals: Understanding of algebra and basic statistics.
  • Logical Thinking: Ability to approach problems step by step.
  • Programming Curiosity: Interest in learning coding from scratch.
  • English Proficiency: Ability to follow technical instructions clearly.

Exam Details

Passing Score

70%

Format

50 multiple-choice/multiple-response questions

Exam Blueprint

Introduction to Vibe Coding & AI Tools 15%
Prompting for Code - Basics & Best Practices 15%
Debugging & Testing via AI 15%
Building a Simple Full-Stack App with Prompts 20%
Code Ethics, Security, and AI Limits 20%
Capstone Project - Prompt-Driven App 15%
Self-Paced Online

Self-Paced: 4 hours of content

USD $ 195.00
Purchase Self-Paced Course
Instructor-Led Online

Instructor-Led: 1 day (live or virtual)

USD $ 1110.00
Purchase Instructor-Led Course

Core AI Tools Covered

Python

Python

TensorFlow

TensorFlow

PyTorch

PyTorch

GitHub Copilot

GitHub Copilot

OpenAI Codex

OpenAI Codex

Hugging Face Hub

Hugging Face Hub

LangChain

LangChain

FastAPI

FastAPI

VS Code

VS Code

Jupyter Notebooks

Jupyter Notebooks

Pandas

Pandas

NumPy

NumPy

Scikit-learn

Scikit-learn

Docker

Docker

Streamlit

Streamlit

API Integration Tools

API Integration Tools

Prompt Engineering Frameworks

Prompt Engineering Frameworks

Automation SDKs

Automation SDKs

Version Control Systems (Git)

Version Control Systems (Git)