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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.

Enroll Now

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