Generative AI & Prompt Engineering Masterclass

15,990.00

CandidatureDurationProspect

    1. Aspiring AI and ML Professionals: Individuals who have a passion for Artificial Intelligence and Machine Learning and want to build a strong foundation in the field to pursue a career as AI/ML engineers, data scientists, or AI researchers.
    2. Software Developers and Programmers: Professionals already working in the software development field who want to upskill and specialize in AI/ML to leverage the growing demand for AI-driven solutions in various industries.
    3. Data Analysts and Data Scientists: Professionals with a background in data analysis who want to enhance their skills and expand their knowledge to include advanced AI and ML techniques for more accurate and insightful data-driven decision-making.
    4. IT and Technology Managers: Managers and leaders responsible for implementing AI/ML solutions within their organizations, seeking to gain a deeper understanding of the technologies involved to make informed strategic decisions.
    5. Entrepreneurs and Innovators: Individuals interested in leveraging AI and ML to develop innovative products or start their own AI-driven ventures, requiring a comprehensive understanding of AI/ML concepts and practical implementation.
    6. Graduates and Postgraduates: Recent graduates or postgraduates in computer science, engineering, or related fields looking to specialize in AI and ML to enhance their employability and job prospects in the competitive tech industry.
    7. Professionals from Diverse Fields: Individuals from fields such as finance, healthcare, marketing, and manufacturing who want to leverage AI/ML to drive innovation, improve decision-making, and gain a competitive edge in their respective industries.

The estimated duration of this course will be 12-16 weeks. This is the optimal duration that offers students a balanced learning experience while accommodating other commitments and providing ample time for practice and project work . Here is the breakdown of the course duration :-

  • Number of Sessions per Week: 3 sessions per week
  • Duration of Each Session: 2 hours per session
  • Total Course Hours: 36 to 48 hours

By allocating 2 hours per day, students can effectively cover the course content, engage in hands-on activities, and participate in discussions and interactions with instructors and peers.

Job opportunities will include roles such as Cloud Architect, Cloud Engineer, Cloud Consultant, and more. These roles offer attractive remuneration packages, career growth prospects, and the chance to work on cutting-edge cloud projects. According to our research through some reliable job portals here are the salary survey figures in Indian MNCs based on available industry reports :-

  1. Cloud Architect: In Indian MNCs, the salary for a Cloud Architect can range from INR 15 lakh to INR 30 lakh per annum. Senior-level Cloud Architects with extensive experience and expertise may earn salaries upwards of INR 40 lakh per annum.
  2. Cloud Engineer: For a Cloud Engineer role in an Indian MNC, the salary can range from INR 8 lakh to INR 20 lakh per annum. Entry-level Cloud Engineers can expect salaries around INR 6 lakh to INR 10 lakh per annum, while experienced professionals can earn salaries exceeding INR 25 lakh per annum.
  3. Cloud Consultant: The salary for a Cloud Consultant in Indian MNCs can vary based on experience and expertise. On average, Cloud Consultants earn salaries ranging from INR 10 lakh to INR 20 lakh per annum.

Salaries can be even higher than 40 LPA for individuals with niche skills, and a proven track record of successfully implementing and managing multi-cloud solutions. Our aim is to empower you by providing practical learning , industry relevant skills and career support so that you can prove your value during job interviews and able to secure a good job with a competitive salary in this field.

 

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Description

Course Outline
Module 1: Python Essentials for AI
  1. Python Data Types, Loops, Functions

  2. Data Structures in Python (Lists, Dicts, Sets)

  3. NumPy & Pandas for Data Handling

  4. Mini Project: Data cleaning & JSON-based prompt generator
Module 2: Foundations of Generative AI
  1. What is Generative AI? How it differs from traditional ML

  2. Applications in images, text, video, and code generation

  3. Types of GenAI Models: VAEs, GANs, Diffusion Models, Transformers

  4. Key Concepts: Embeddings, Sampling, Latent Space

  5. Hands-on: Try DALL·E2, Stable Diffusion, Midjourney, and RunwayML

Module 3: LLMs and Transformer-based Architectures
  1. Understanding Attention & Transformers

  2. LLM Ecosystem: GPT-4, Gemini, Claude, LLaMA, Falcon

  3. Pre-training, Fine-tuning & RLHF

  4. Self-hosted LLMs using Hugging Face, Ollama

  5. Hands-on: Query GPT/OpenAI & HuggingFace models via API

Module 4: Prompt Engineering Essentials
  1. Prompt Design Strategies: Zero-shot, Few-shot, CoT

  2. Chat Prompts vs Completion Prompts

  3. Hands-on with OpenAI Playground

  4. Image Prompting for DALL·E & Midjourney

  5. Common Pitfalls and Prompt Optimization Tips

Module 5: LangChain and RAG
  1. What is LangChain and Why Use It?

  2. LangChain Components: Chains, Agents, Memory

  3. Retrieval-Augmented Generation (RAG)

  4. Hands-on: Build your own Q&A bot over a PDF or website

Module 6: Building LLM-Powered Applications
  1. Architecting LLM Workflows

  2. Vector Databases: FAISS, ChromaDB

  3. Embedding Models & Search

  4. Benchmarking and Evaluating LLMs

  5. Prompt Injection & Guardrails

Module 7: Agentic AI & Autonomous Workflows
  1. Agent Concepts: Actions, Goals, Memory

  2. LangGraph, CrewAI, AutoGen Overview

  3. Hands-on: Create a multi-agent system using LangChain or CrewAI

Module 8: Generative AI on the Cloud
  1. OpenAI on Azure, Gemini on Google Cloud

  2. Deploying LLMs in AWS Sagemaker / GCP Vertex AI

  3. Fine-tuning on the Cloud

  4. Integrate LLM APIs with Web/Mobile apps

Module 9: Generative AI for Images & Videos
  1. Intro to Diffusion Models & VAEs

  2. Autoencoders, Shared Embeddings

  3. Hands-on Tools: Stable Diffusion, RunwayML, Synthesia

  4. Contrastive Learning Techniques

  5. Use Cases: Ad generation, avatar creation, branding assets

Module 10: AI Governance & Ethics
    • Ethical AI Design Principles

    • AI Governance Structures & Lifecycle

    • Risks of Deepfakes, Bias & Hallucinations

    • Industry Guidelines (EU AI Act, NIST, etc.)

    • Future Trends in AI Regulation

Module 11: Capstone Project
  1. Build a GPT-powered FAQ bot with document retrieval

  2. Create a product description generator with OpenAI & LangChain

  3. Design a chatbot agent using CrewAI + embedding store

  4. Image-to-story creative writing generator using DALL·E & LLM

 

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