- GenAI Unleashed: Generative AI – Detailed
This industry-focused AI program covers Python, Machine Learning, Deep Learning, Generative AI, RAG systems, Agentic AI, and multimodal technologies. Learners build real-world projects, deploy production-ready models, and master modern AI workflows—gaining the skills needed to become job-ready AI engineers, data scientists, and GenAI specialists.
Course Info
Module 1: Prerequisites & Tools
This module introduces core data tools used in analytics and AI. Students learn Python, Pandas, SQL, and Power BI to clean, analyze, and visualize data effectively using real-world business use cases.
Module 2: Core Machine Learning
Students explore the foundations of supervised and unsupervised learning, covering regression, classification, clustering, and model evaluation. Real ML workflows help them understand how to build predictive systems that solve real business problems.
Module 3: Deep Learning & NLP
This module builds a strong foundation in neural networks and natural language processing. Students learn CNNs, RNNs, text processing, embeddings, and apply them in practical NLP tasks like sentiment analysis.
Module 4: Generative AI Fundamentals
Students dive into transformers, LLMs, prompting techniques, and modern GenAI concepts. They work with models on Hugging Face and build applications such as automated content generation tools.
Module 5: Retrieval-Augmented Generation (RAG)
This module teaches how to build intelligent knowledge-augmented systems using RAG architecture, embeddings, vector databases, and document processing. Students create production-ready RAG applications like internal enterprise chatbots.
Module 6: Model Deployment & Ethics
Students learn how to bring AI systems to production using MLOps fundamentals. They also explore responsible AI, safety guardrails, and ethical considerations before deploying models as web services.
Module 7: Capstone Project 1 (ML/DL Focus)
A complete end-to-end project where students choose from ML, DL, or NLP domains. They build a full pipeline involving data preparation, model development, evaluation, and deployment with a final project report.
Module 8: Capstone Project 2 (GenAI/RAG Focus)
Students design and implement a full GenAI application or RAG system. They work through a complete GenAI pipeline and deliver practical AI applications such as custom RAG chatbots.
Module 9: Practical Agentic AI
This module introduces AI agents, their architecture, and the perception–action loop. Students build functional agentic systems using LangChain or AutoGen with tool-use capabilities and autonomous reasoning.
Module 10: Multimodal Basics
Students explore how AI models integrate vision, text, and audio. They learn CLIP, LLaVA, Whisper, and basic multimodal generation to build applications like automated image captioning and accessibility tools.
Material Includes
Learners will receive:
Comprehensive eBooks, PDFs, and module-wise study guides
Hands-on notebooks (Python, ML, DL, NLP, GenAI)
Real datasets for practice and projects
Access to coding assignments & practical labs
Industry-level case studies and real-world examples
Video lectures and recorded sessions
Free access to GenAI tools, LLM playgrounds, and no-code AI platforms
Pre-built templates for MLOps, deployment, and agent-based systems
Source code for all end-to-end projects
Certification upon completion of final assessments
Requirements
To enroll in this course, learners should have:
Basic computer operation skills (Windows/Mac/Linux)
A laptop with minimum specifications:
8 GB RAM (16 GB recommended)
i5/Ryzen 5 or equivalent processor
Stable internet connection
No prior programming or mathematics experience is compulsory—everything is taught from scratch
Optional but useful:
Basic understanding of logical reasoning or school-level math
Curiosity about technology, automation, and AI