Building Generative AI Apps on Google Cloud - Skillioma Learn 21ˢᵗ Century Skills

About Course

Course Objectives:

Understand the basic concepts and terminology of AI
Explore the history and evolution of AI
Learn about different types of AI, including machine learning, deep learning, and natural language processing
Understand the practical applications of AI in various fields such as healthcare, education, business, and creativity
Develop awareness of ethical considerations and bias in AI
Gain hands-on experience with AI tools and projects

Key Features:

Quizzes: Test your understanding of key concepts with engaging quizzes throughout the course
Projects: Apply what you’ve learned in real-world projects that reinforce your knowledge and skills
Assignments and Exercises: Complete various assignments and exercises designed to deepen your understanding of AI topics
Final Project: Analyze real-world scenarios to see how AI is being used in different industries
Certification Rubric: Measure your knowledge before and after the course to track your progress and learning outcomes

Course Duration: This course is designed to be completed over 6 weeks, with an estimated study time of 50–60 hours. This flexible schedule allows you to learn at your own pace while covering all the essential topics and engaging in hands-on activities.

Learning Outcomes: By the end of this course, you will be able to:

Define and explain key AI concepts and terminology
Identify and describe various applications of AI in real-world scenarios
Understand the process of machine learning and how algorithms work
Discuss the ethical implications and challenges of AI
Utilize AI tools and frameworks in hands-on projects
Analyze and interpret AI-generated data and outputs

Who Should Enroll: This course is perfect for anyone interested in learning about AI, including:

Students – College (Intermediate, Degree), University, and High School
Working Professionals, Beginners, Freshers.
Adults that are returning to work or any adult who wants to update their skills.
Educationists – Teachers, Professors, anyone in the teaching profession.

Join Us: Enroll now and start your learning adventure with us!

Course Content

Module 1: GenAI Development Essentials (Week 1)
Goal: Onboard all learners with foundational concepts and tools used across all platform tracks.

What is Generative AI and How Are Apps Built with It
Large Language Models vs. Generative Apps: Who Builds What?
Introduction to Prompt Engineering (with ChatGPT or Gemini)
Creating a GitHub Account + Repo Basics
Free Coding Environments: Google Colab, Streamlit, Replit
GenAI Fundamentals
Exercise: Create your first prompt + GitHub repo for this course
Assignment: “Write a real-world use case for an AI assistant” (to reuse later)

About Course

Course Content

Module 1: GenAI Development Essentials (Week 1) Goal: Onboard all learners with foundational concepts and tools used across all platform tracks.

What is Generative AI and How Are Apps Built with It

Large Language Models vs. Generative Apps: Who Builds What?

Introduction to Prompt Engineering (with ChatGPT or Gemini)

Creating a GitHub Account + Repo Basics

Free Coding Environments: Google Colab, Streamlit, Replit

GenAI Fundamentals

Exercise: Create your first prompt + GitHub repo for this course

Assignment: “Write a real-world use case for an AI assistant” (to reuse later)

Module 2: Getting Started with Google Cloud & Gemini (Week 2) Goal: Get comfortable using Google Cloud’s free AI tools.

Signing Up for Google Cloud (free tier + Vertex AI setup)

What is Gemini? PaLM? Vertex AI Studio?

Exploring Vertex AI Studio Playground (Gemini chat, code, multimodal)

First Gemini API Call (using Python + Colab)

Google Cloud and Gemini

Hands-On Lab: Make a Gemini API call with Python in Colab

Assignment: Build a mini tool that summarizes text using Gemini

Module 3: Prompting, APIs, and Retrieval (Week 3) Goal: Build functional prompt chains, call APIs, and understand context windows.

Prompt Engineering Deep Dive: Roles, Temperature, Few-Shot, Chain-of-Thought

Using Gemini APIs with Python: Tokens, responses, parameters

Calling External APIs in Gemini-Integrated Apps

Intro to RAG (Retrieval-Augmented Generation)

Prompting & API Use

Hands-On Lab: Build a custom Q&A tool using Gemini + a CSV knowledge base

Assignment: Compare 3 prompt styles for the same task. Evaluate outputs.

Module 4: Building Interactive AI Apps (Week 4) Goal: Build usable front ends with tools like Streamlit, HTML, Gradio.

What is Streamlit? Setup + Deployment Options (Streamlit Cloud)

Create a Front-End UI for Gemini-powered Apps

Create Interactive AI Web Forms (Data Q&A, Email Writer, etc.)

Connect GitHub Repo to Deployable Web App

App Building Tools

Hands-On Lab: Build an interactive Streamlit app with Gemini backend

Assignment: Build a resume reviewer app using Gemini + UI

Module 5: Embeddings, Vector Search & Use Cases (Week 5) Goal: Explore vector embeddings, use with BigQuery or free vector DB.

What Are Embeddings? Why Use Them?

Creating & Visualizing Text Embeddings (with Google API or sentence-transformers)

Searching Embeddings (FAISS, Google BigQuery, or Weaviate Free Tier)

Use Case Deep Dive: Travel Planner, Health Assistant, Internal Docs Chat

Embeddings and Search

Hands-On Lab: Build a knowledge base with embeddings + Gemini

Assignment: Design an AI agent that can search your project folder for answers

Module 6: Final Project + Deployment + Certification (Week 6) Goal: Capstone week to consolidate skills and deploy final project.

Final Project Brief: AI Productivity Assistant / Chatbot

Deployment Options: Streamlit, Replit, Hugging Face Spaces

GitHub Repo Cleanup + Documentation Tips

Scoring Rubric + Peer Feedback (if in cohort setting)

Final Project (choose 1 theme):

AI Assistant for Students

AI Form Assistant (travel, resume, feedback)

Knowledge Base Chatbot for a PDF/Doc Set

Deliverables:

Deliverables:

COMPANY

COURSES

CONTACT

Module 1: GenAI Development Essentials (Week 1)
Goal: Onboard all learners with foundational concepts and tools used across all platform tracks.

Module 2: Getting Started with Google Cloud & Gemini (Week 2)
Goal: Get comfortable using Google Cloud’s free AI tools.

Module 3: Prompting, APIs, and Retrieval (Week 3)
Goal: Build functional prompt chains, call APIs, and understand context windows.

Module 4: Building Interactive AI Apps (Week 4)
Goal: Build usable front ends with tools like Streamlit, HTML, Gradio.

Module 5: Embeddings, Vector Search & Use Cases (Week 5)
Goal: Explore vector embeddings, use with BigQuery or free vector DB.

Module 6: Final Project + Deployment + Certification (Week 6)
Goal: Capstone week to consolidate skills and deploy final project.