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Curriculum of CVOC in Artificial Intelligence

A structured 10-week, industry-focused program designed to make you job-ready through real-world projects, tools, and hands-on execution.

Program Overview

This program spans over 2 months+15 days and is divided into structured weekly modules focused on practical skills and real execution.

Duration

10 Weeks (2 Months + 15 Days)

Mode

Live Online + Practical Assignments

Outcome

Job-ready skills with hands-on experience

Who This Is For

Students, freshers, and professionals switching careers

Weekly Curriculum Breakdown

Each week builds progressively with real-world AI systems, vision pipelines, and RAG execution.

Week 1 – AI Foundations for Vision & Retrieval Systems

AI vs ML vs Deep Learning (industry perspective)
Real-world use cases of image tagging & RAG
Understanding unstructured data (images, text, metadata)
High-level architecture of AI retrieval systems
How embeddings & similarity power AI systems

Week 2 – Computer Vision Essentials (Image Understanding)

Digital image fundamentals (pixels, channels, color spaces)
Feature extraction & visual representations
CNNs vs Vision Transformers (ViT)
Pre-trained vision models (conceptual understanding)
Bias, misclassification & limitations of vision models

Week 3 – Image Tagging Systems (Concept to Execution)

Image classification vs object detection vs captioning
Single-label & multi-label tagging systems
Vision-language models (CLIP – conceptual)
Semantic tag generation & metadata design
Confidence scoring & error handling strategies

Week 4 – Embeddings & Semantic Search (Bridge to RAG)

Image embeddings vs text embeddings
Vector similarity & distance metrics
Cross-modal embeddings (image ↔ text)
Vector databases & indexing concepts
Semantic vs visual similarity search

Week 5 – Retrieval Augmented Generation (RAG) Fundamentals

Why traditional LLMs fail without retrieval
RAG vs fine-tuning
Core RAG architecture components
Chunking strategies & retrieval logic
Enterprise use cases of RAG systems

Week 6 – Multimodal RAG (Text + Image)

What is multimodal AI?
Image-to-text pipelines for retrieval
Image captions as retrieval anchors
Metadata-driven multimodal search
Context injection strategies for LLMs

Week 7 – RAG Optimization, Accuracy & Trust

Advanced chunking & query rewriting
Re-ranking & retrieval accuracy improvements
Hallucination control techniques
Grounded responses & citations
Security & data privacy in RAG systems

Week 8 – Production Architecture & System Design

Scalable architecture for RAG pipelines
Offline vs real-time processing
Latency, throughput & cost optimization
Monitoring AI systems in production
Versioning models, data & embeddings

Week 9 – Capstone Project (Industry Use Case)

AI Image Tagging for Ecommerce
Media asset search using multimodal RAG
Document + image knowledge systems
End-to-end system architecture & data flow
Retrieval logic & RAG reasoning design

Week 10 – Evaluation, Ethics & Corporate Readiness

AI ethics & bias in vision systems
Copyright, compliance & data ownership
Responsible AI deployment practices
Explaining AI decisions to stakeholders
Final project presentation & evaluation

Ready to Build Real Skills?

Join the Corporate Vocational Program and start working on real projects.

Tools & Technologies You Will Work With

Python Computer Vision Libraries Vision Transformers (ViT) CLIP (Vision-Language Models) Vector Databases Embedding Models Large Language Models (LLMs) Multimodal RAG Pipelines System Architecture Design

At ZeusInfinity Services, we empower professionals through hands-on, industry-aligned training focused on digital skills and career growth.

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