AI Portfolio in 2025: Build 12 AI end-to-end AI Use Cases

AI Portfolio in 2025: Build 12 AI end-to-end AI Use Cases udemy course free download

Get ahead with the hands-on experience of the most essential skill of 2025- AI Literacy (Gen AI & Agentic AI Included)

The AI Literacy Specialization Program is one-of-a-kind hierarchical & cognitive skills based curriculum that teaches artificial intelligence (AI) based on a scientific framework broken down into four levels of cognitive skills.

Part 2: Use & Apply combines the below two cognitive skills -

• Using (practicing AI concepts in realistic environments)

• Applying (adapting AI knowledge to solve real-world problems)

This part of the program emphasizes practical implementation and hands-on skill-building through structured exercises and applied use cases. It includes 3 core competencies, each supported by detailed performance indicators, totaling 20. These are designed to ensure learners are able to confidently navigate and apply AI technologies in varied contexts.

Competency Overview

1) Traditional AI

This competency focuses on foundational AI methods developed before the deep learning era and includes core machine learning approaches. Learners will understand the end-to-end AI workflow and the different layers involved in building traditional AI systems.

Performance Indicators:

• Understanding the AI Technology Stack

• Application Layer: User interface and business application logic

• Model Layer: Machine learning algorithms and training logic

• Infrastructure Layer: Cloud platforms, hardware accelerators, and deployment tools

• Common Components: Data pipelines, model monitoring, and governance

• Choosing the Right Tech Stack for Business Use Cases

• End to end Use Cases:

• Credit Card Default Prediction

• Housing Price Prediction

• Segmentation for Online Retail

• NLP Based Resume to JD Matcher

• CV Based Car Type Detection

2) Generative AI

This competency introduces learners to cutting-edge generative AI tools and techniques, including how large language models (LLMs) and diffusion models are built and adapted. The focus is on responsible usage, design of prompts, and system integration.

Performance Indicators:

• Understanding the Generative AI Technology Stack

• Prompt Engineering (PE) – Basics (Prompt types, templates, prompt chaining)

• Resume Customizer Tool

• Ideation with ChatGPT

• Design using Gamma

• Build and Deploy using Lovable

• Market with HubSpot

• Maintain with Gemini for Sheets

• Prompt Engineering – Advanced (Context management, few-shot prompting, evaluation)

• Resume Customizer Tool using API

• Retrieval-Augmented Generation (RAG) – Using external knowledge with LLMs

• RAG Based Resume to JD Matcher

• Fine-tuning – Customizing pre-trained models for specific enterprise or domain needs

3) Agentic AI

This competency focuses on the emerging paradigm of AI agents – systems that can reason, plan, and act autonomously within defined boundaries. It helps learners understand how to orchestrate multi-step tasks using AI tools.

Performance Indicators:

• Understanding the Agentic AI Architecture

• Vibe Coding 101

• No Code Agent Builders

• AI News Summarizer:

• Using ChatGPT UI & CustomGPT Builder

• Using Replit

• Using n8n

• Code Based Agentic AI

• Credit Card Default Prediction using Cursor

• Agentic AI in the Workplace

By completing Part 2 of the AI Literacy Specialization Program, participants will:

• Gain practical experience in building and deploying AI models across different domains

• Be equipped to select and apply the right AI techniques for specific business problems

• Understand the technical and ethical dimensions of applying both traditional and generative AI

• Be capable of designing AI workflows and interfacing with technical teams confidently

• Build readiness to transition into advanced AI roles or contribute meaningfully to AI projects in non-technical roles