Course Modules
Module 1: Foundations of Generative AI in PLM
- Introduction to Gen AI and its evolution
- Overview of machine learning and deep learning basics
- Why Machine Learning & Deep Learning is Still Relevant?
- Building Using GenAI
- Understanding the Future of GenAI in PLM
- Revisit Machine Learning
- Revisit Deep Learning
- Project - Building AI Agents in PLM ( Feature , Product ideation )
Module 2: RAG (Retrieval Augmented Generation)
- Vector databases
- Embedding techniques
- Chunking strategies
- Building Using GenAI
- Context optimization
- Knowledge base creation
- Project - Building AI Chatbot on dataset
Module 3:AI Agents
- Prompt engineering
- What is AI Agent
- When to build AI Agent
- Agent Design Foundation
- Build Your First AI Agent
- Single Agent System
- Multi Agent System
- Single Vs Multi Agent System
- Guardrails
- Project Real world usecase
Module 4: Deep Dive into AI Agents
- Agents vs chatbots: What’s the difference?
- LangChain & LangGraph basics
- MCP Protocol
- Example of MCP
- Project - Automation testing using MCP
- Vibe coding
- A2A Protocol
- A2A and MCP
- Project :Teamcenter Agent using A2A and MCP
- Building task-driven AI agents
Module 3: Vibe Coding and Workflow Automation
- Introduction to Vibe Coding and GenAI Development Revolution
- Setting up AI-Powered Development Environment
- Mastering Prompt Engineering for Code Generation
- Workflow automation agent platform - n8n
- Project - Real world app development using vibe coding
Module 5: Deploying AI Agents in Production
- Containerizing AI Agents with Docker
- Hosting AI Agents on Cloud and locally
- Scaling AI Agents for High-Volume Workloads
- Implementing Secure AI Agent Deployments
- Monitoring & Maintaining AI Agents in Production
Course Includes
LIVE Sessions
Quizzes & Projects
Learning Materials
Projects & Case Studies
Course Modules
Module 1: Statistics and Mathematics for DS Interviews
- Probability and Statistical Testing Questions
- Linear Algebra Interview Problems
- Hypothesis Testing Scenarios
- Time Series Analysis Questions
- Statistical Modeling Case Studies
Module 2: Machine Learning Fundamentals
- Supervised Learning Algorithm Questions
- Unsupervised Learning Problem Solving
- Model Selection and Evaluation Scenarios
- Feature Engineering Interview Questions
- ML Pipeline Design Discussions
Module 3: Deep Learning and Neural Networks
- Neural Network Architecture Questions
- CNN and Computer Vision Problems
- RNN and NLP Interview Topics
- Transfer Learning Applications
- Model Optimization Scenarios
Module 4: Generative AI and LLMs
- Transformer Architecture Questions
- Prompt Engineering Techniques
- Fine-tuning and RAG Implementation
- LLM Evaluation Methods
- GenAI Project Case Studies
Module 5: Data Engineering and MLOps
- Data Pipeline Design Questions
- Model Deployment Scenarios
- ML System Design Problems
- Model Monitoring Interview Topics
- Data Versioning and ML Tracking
Course Includes
LIVE Sessions
Quizzes & Projects
Learning Materials
Projects & Case Studies