Advanced Generative AI Training in Hyderabad
With 100% Placement Assistance
Brolly AI provides Generative AI Training in Hyderabad designed to help you confidently move into Artificial Intelligence. If you are a career switcher, job seeker, or non-IT professional, we guide you step by step with practical projects and real skills. Our placement support prepares you for interviews and helps you successfully step into Generative AI job roles.
- 15+ Years Industry Experience
- Capstone Real-Time Projects
- Resume & Interview Preparation
- Flexible Batch Timings
Book Your Free Generative AI Demo Class
Professional Generative AI Course Program Overview
We provide Generative AI Training in Hyderabad with a strong focus on practical learning and real industry requirements. We created this program for career switchers, job seekers, and non-IT professionals who want to build a career in Artificial Intelligence.We begin with Python basics and core Machine Learning, and then we guide you step by step into Large Language Models, Prompt Engineering, RAG systems, vector databases, and real AI application development. We explain every concept in a simple way so you can clearly understand how AI works and how it is used in real companies. Our training includes hands-on projects, industry-based certification, portfolio development, and full placement support to help you secure Generative AI job opportunities in Hyderabad.
Next Batch Details
Next Batch Starts
25th February 2026
Session Time
07:10 AM to 08:10 AM
Course Duration
3 Months
Course Fees
Online – 35k & Offline – 40k
Trainer Experience
15+ Years Industry Experience
Training Modes
Offline & Online, Corporate Training
Generative AI Course Curriculum
- Python Basics
- Introduction to Python
- Installation (Google Colab / Jupyter)
- Variables and Constants
- Comments
- Primitive Data Types
- Type Casting
- String Operations
- Data Structures
- Lists
- Tuples
- Sets
- Dictionaries
- List Comprehension
- Match Statement
- Control Flow
- If, elif, else
- Nested Conditions
- Logical Operators
- For Loop, While Loop
- Break, Continue
- Functions & Advanced Concepts
- Functions
- Lambda Functions
- map(), filter(), reduce()
- Generators & Iterators
- Object-Oriented Programming (OOP)
- Class & Object
- init Method
- Inheritance
- Polymorphism
- Encapsulation & Abstraction
- Static Methods
- Access Modifiers
- Method Overriding
- Modules & Exception Handling
- Module Creation
- Importing Modules
- Exception Handling
- Multithreading
- Python Libraries
- NumPy
- Pandas
- Matplotlib
- Seaborn
Project
Final Project – To-Do Application
Mock Tests & Assignments
Statistics Topics
- Introduction to Statistics
- Data & Types of Data
- Population & Sample
- Sampling Methods
- Descriptive Statistics
- Mean, Median, Mode
- Variance, Standard Deviation
- Distribution Shape
- Graphical Visualization
- Inferential Statistics
- Estimation of Parameters
- Confidence Intervals
- Hypothesis Testing
- Statistical Tests (t-test, ANOVA, Chi-square)
- Regression Analysis
- Correlation & Association
Probability Topics
- Trial & Outcome
- Sample Space
- Types of Events
- Random Variables
- Permutations & Combinations
- Conditional Probability
- Bayes’ Theorem
- Probability Distributions
- Machine Learning Foundations
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Structured vs Unstructured Data
- Confusion Matrix
- Model Evaluation Metrics
- Data Preparation
- Data Cleaning
- Handling Missing Values
- Outlier Detection
- Feature Scaling
- Train-Test Split
- Regression Algorithms
- Linear Regression
- Multiple Regression
- Ridge, Lasso, ElasticNet
- Classification Algorithms
- Logistic Regression
- Naive Bayes
- k-Nearest Neighbors
- Decision Trees
- Random Forest
- Boosting Methods
- Support Vector Machines
- Feature Engineering
- Feature Creation
- Feature Selection
- Dimensionality Reduction (PCA, t-SNE)
- Hyperparameter Tuning
- NLP & Text Mining
- Text Preprocessing
- Tokenization
- Stopword Removal
- Lemmatization & Stemming
- Bag of Words
- TF-IDF
- Text Classification
Capstone Project
- End-to-End ML Project
Topics Covered
- Artificial Neural Networks (ANN)
- Activation Functions
- Backpropagation
- Optimisers (SGD, Adam)
- Convolutional Neural Networks (CNN)
- Transfer Learning
- Recurrent Neural Networks (RNN)
- LSTM & GRU
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAE)
- Transformer Architecture
- Attention Mechanism
- Pretrained Models (BERT, GPT, Vision Transformers)
Topics Covered
- Generative AI Fundamentals
- What is Generative AI
- Applications (Text, Image, Code)
- Introduction to LLMs
- Pre-training & Fine-tuning
- Word Embeddings
- Hugging Face & Model Usage
- Hugging Face Hub
- Pipelines
- Manual Model Loading
- Fine-Tuning
- Model Evaluation
- Model Deployment Basics
- LangChain
- Prompt Templates
- Chains
- Output Parsers
- Memory
- Agents
- LangGraph
- Graph-Based Control Flow
- Nodes & Edges
- State Management
- Conditional Logic
- Human-in-the-loop
- Retrieval-Augmented Generation (RAG)
- RAG Architecture
- Vector Databases (FAISS, Chroma)
- Document Loaders
- Embeddings
- RAG Q&A Chain
- RAG Evaluation
- Advanced GenAI Applications
- Multi-turn Chatbots
- Multi-tool Agents
- Planning vs Reactive Agents
Topics Covered
- What is Agentic AI
- AI Agent Architecture
- Agent Lifecycle (Think → Plan → Act → Observe)
- Chain-of-Thought & ReAct
- LangChain Agents
- Multi-Agent Systems
- Model Context Protocol (MCP)
- MCP Architecture
- MCP Servers & Clients
- Context & State Management
Topics Covered
- Principles of Effective Prompting
- Zero-shot Prompting
- Few-shot Prompting
- Chain-of-Thought Prompting
- Structured Outputs (JSON, Tables)
- Prompt Optimisation & Evaluation
Deploy and manage Generative AI solutions using modern platforms and tools.
- OpenAI & Hugging Face
- TensorFlow & PyTorch
- Cloud-based AI deployment workflows
- Enterprise-grade AI toolchains
What is Generative AI?
Generative AI is a branch of Artificial Intelligence that can create new content such as text, images, code, and smart responses. Instead of only analyzing data, it learns patterns from large datasets and generates useful output. It is powered by models like Large Language Models and deep learning systems used in modern AI applications.
Key Benefits | Industry Examples |
Creates content quickly and accurately | IT companies build chatbots and AI assistants |
Automates repetitive tasks | Banking uses it for customer service automation |
Improves productivity in daily work | Marketing teams use it for content creation |
Supports decision-making with smart responses | Healthcare uses it for medical report support |
Why Choose Career in Generative AI?
1. High Demand in the Job Market
- Many companies are using AI tools in daily work.
- There is a growing need for skilled AI professionals.
- Job openings are increasing across different industries.
2. Strong Salary Growth
- AI roles offer better salary compared to many entry-level jobs.
- Skilled professionals can see steady income growth.
- Experience in AI increases your long-term earning potential.
3. Opportunity to Switch Careers
- You can move from a non-IT background into AI with proper training.
- Career switchers can build a new technical profile step by step.
- Your dedication matters more than your previous degree.
4. Work on Modern Technology
- You learn how intelligent systems are built and used.
- AI tools are shaping the future of business and technology.
- You gain skills that are relevant for the next decade.
5. Practical and Creative Work
- You build systems that generate content and solve real problems.
- The work is not repetitive; it requires thinking and problem-solving.
- You see direct results from the applications you create.
6. Long-Term Career Stability
- AI is becoming part of every industry, not just IT.
- Skills in AI remain valuable as technology evolves.
- With continuous learning, you can grow into senior roles over time.
Real-Time Generative AI Projects & Practical Training in Hyderabad
AI-Based Resume Screening System
Skills Used: Python, NLP basics, Hugging Face models, Prompt design
Intelligent Chatbot with Memory
Skills Used: OpenAI API, LangChain, conversation memory, API integration
Document Question & Answer System (RAG)
Skills Used: RAG concept, vector database, embeddings, LLM integration
AI Content Generation Tool
Skills Used: Prompt Engineering, LLM Usage, Output Optimization
AI Task Automation Workflow
Skills Used: AI Agents, Workflow Design, Tool Integration
End-to-End AI Application Deployment
Skills Used: Model Integration, Basic Deployment, Environment Setup
Industry Tools Covered in Our Generative AI Course
TensorFlow
TensorFlow helps you build, train deep learning models, and understand real AI systems.
PyTorch
PyTorch helps you build neural networks and transformer models, and test AI models easily.
Python
Python is the base of Generative AI; learn coding basics and build simple AI programs.
Hugging Face
Work with pre-trained Large Language Models for text generation, summarization, and chatbot development.
OpenAI GPT
Use OpenAI APIs to integrate language models, build chatbots and automation tools.
LangChain
LangChain links models with workflows and data, helping you build step-by-step AI agents.
Pandas & NumPy
These tools help clean, prepare data, and confidently handle structured datasets.
Google Colab
Run AI projects in the cloud without installing heavy software on your system.
Docker
Docker helps you package and deploy AI applications, and understand basic deployment concepts.
Learning Modes Generative Ai Training
Online Training
- Lifetime video access
- Basic to advanced Generative AI modules
- Live interactive classes
- Weekly mock interviews
- Digital Learning Modules
- Online Practical Labs
- Flexible Learning Schedules
- 100% placement assistance
Classroom Training
- 3 months structured classroom training
- Expert Generative AI trainers
- Real-time industry projects
- One-on-one mentorship & lab support
- Monthly mock interviews
- Resume building & interview guidance
- Covers Advanced Topics
Corporate Training
- Customized Training Programs
- Daily Recordings
- Doubt-clearing sessions
- Expert Instruction
- Industry-Relevant Content
- Performance Monitoring
- On-Site Workshops
- 1 Capstone project included
Meet Your Generative AI Trainer
INSTRUCTOR
Mr. Dinesh
Generative AI Specialist & LLM Architect
Experience: 12+ Years in Artificial Intelligence, Machine Learning & Generative AI
Teaching Style: Explains complex Generative AI concepts like transformers, embeddings, and AI agents in simple language with hands-on coding and job-focused training
Core Expertise: Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), AI Agents, NLP systems, Deep Learning, MLOps, and Cloud deployment (AWS)
Industry Projects: Designed LLM-based AI assistants, RAG-powered knowledge platforms, intelligent chatbots, fraud detection systems, and predictive AI solutions for healthcare and manufacturing sectors
Generative AI Training Highlights
LLM Creation & Testing
Learn all about creating and testing language models, from the basics to advanced techniques.
Start with Python
Begin with Python and move on to learn about Machine Learning, Deep Learning, NLP, Language Models, and MLOps.
Advanced NLP Testing
Industry-Ready Curriculum
Our course is designed to meet current industry needs, covering the latest and most relevant topics.
Experienced Trainers
Foundation Level Training
Adaptive Study Methods
Interactive Classes
Lifetime Access
Practical Implementation
Doubt Clarification Sessions
Personalized Mentorship
Advanced Topics
Continuous Support
Interview Preparation
Accredited Certification
Earn a recognized certification that showcases your skills and knowledge.
Access to Research Papers
Placement Assistance
Success Stories from Our Generative AI Students
Testimonials
Generative AI Placement Support For Career Switchers
Step 1: Resume Building for Generative AI Roles
- Create an ATS-friendly resume
- Highlight LLM projects, RAG systems, and AI applications
- Present your skills clearly for Generative AI jobs
Step 2: LinkedIn Profile Optimization
- Improve your profile for AI hiring searches
- Add project details and Generative AI keywords
- Build a professional AI portfolio presence
Step 3: Practical Project Portfolio
- Complete real Generative AI projects
- Build LLM-based applications
- Develop RAG and AI Agent workflows
- Prepare GitHub-ready code samples
Step 4: Interview Preparation
- Practice common Generative AI interview questions
- Understand LLM architecture and Prompt Engineering
- Learn how to explain RAG systems clearly
Step 5: Mock Interviews
- Attend technical mock interviews
- Get feedback from trainers
- Improve communication and confidence
Step 6: Aptitude & Communication Training
- Logical reasoning practice
- Basic problem-solving
- Improve presentation and confidence skills
Step 7: Internship & Real-Time Exposure
- Work on supervised Generative AI projects
- Gain experience in LLM integration and AI automation
- Strengthen your resume with real use cases
Step 8: Interview Scheduling & Job Referrals
- We connect you with companies hiring for Generative AI roles
- Guidance on applying for LLM and AI developer positions
- Support until you receive an offer
Career Opportunities After Generative AI Training
After completing a structured Generative AI Training program, strong career opportunities become available. Generative AI is a rapidly growing domain, and companies are actively hiring professionals skilled in Large Language Models (LLMs), Prompt Engineering, AI automation, and AI application development. With practical training and project experience, career switchers, job seekers, and non-IT professionals can confidently transition into AI roles.
Job Role | Overview |
Generative AI Engineer | Build and deploy AI applications using LLMs, RAG systems, and AI agents. |
Prompt Engineer | Design, test, and optimize prompts to improve AI model accuracy and output quality. |
Machine Learning Engineer (AI Focused) | Develop, train, and deploy AI models for real-world business applications. |
Data Scientist (AI Specialization) | Use embeddings, vector databases, and data analysis techniques to build intelligent systems. |
AI Software Developer | Integrate Generative AI models into web, mobile, and enterprise applications. |
AI Product Specialist | Support implementation of AI automation tools and AI-driven solutions within organizations. |
Generative Ai Training in Hyderabad
Pre-Requisites
- Before starting Generative Ai training in Hyderabad having a basic understanding of programming languages like Python is helpful.
- Familiarity with fundamental concepts in mathematics, especially algebra and statistics, is beneficial for Understanding the basic Ideas of Generative Ai.
- A foundational knowledge of machine learning concepts, such as supervised and unsupervised learning, provides a solid starting point for learning Generative Ai techniques.
- As Generative Ai involves working with large datasets, a basic understanding of data handling and preprocessing can be beneficial for effective learning.
Brolly AI Professional Certification in Generative AI
At Brolly AI, we give every learner a recognized Generative AI certification that shows your real skills and project work. Our certification helps you strengthen your resume and prepare for roles in LLMs, AI development, and deployment. Whether you choose classroom or online training in Hyderabad, the value of our certification remains the same.
Certifications Offered
- Generative AI Professional Certification
- Large Language Model (LLM) & RAG Systems Certification
- Prompt Engineering & AI Agents Certification
- NLP & Text Intelligence Certification
- AI Model Deployment & Cloud Integration Certification
- Capstone Project & Portfolio Certification
Our Achievements
FAQs
Generative AI training teaches you how to build systems that create text, images, and smart responses using modern AI models like LLMs.
Career switchers, fresh graduates, working professionals, and non-IT learners can join if they are ready to practice and learn step by step
No advanced coding is required. We start with Python basics and guide you clearly until you become comfortable.
Most learners complete the core program in about 3 months with regular practice and project work.
You will learn Python, LLM integration, prompt design, vector databases, and basic AI deployment concepts.
Yes, you will build practical AI projects such as chatbots, document systems, and automation workflows.
Yes, the course is structured so even learners from non-technical backgrounds can understand clearly.
You can apply for roles like Generative AI Engineer, Prompt Engineer, LLM Developer, and AI Automation Specialist.
If you are moving from a non-IT background or changing your career into AI, you can expect ₹5–12 LPA at the entry level.
Yes, structured placement guidance, resume support, and mock interviews are part of the program.
Yes, you will receive a certification after completing training and project requirements successfully.
Only basic math understanding is needed. We focus more on practical implementation than complex formulas.
Yes, flexible schedules allow working professionals to attend classes without leaving their job.
A Large Language Model is an AI system trained on large text data to generate human-like responses.
Prompt Engineering means writing clear instructions so AI models give accurate and useful outputs.
RAG connects AI models with external documents to produce more accurate and context-based answers.
Hyderabad has strong IT companies and startups actively hiring professionals with AI and LLM skills.
Generative AI focuses on creating new content and automation, while Data Science mainly focuses on data analysis and prediction.
Yes, many professionals move into AI roles with structured learning and consistent practice.
Yes, you will build project-based portfolios that help during interviews and job applications.
Yes, mock interviews are conducted to prepare you for real company interview scenarios.
Basic cloud concepts are helpful, and deployment basics are explained during the training.
IT, healthcare, banking, education, marketing, and startups use Generative AI tools widely.
Python is the base language used to build and integrate AI applications effectively.
It may look complex initially, but with guided practice most learners understand it clearly.
Yes, practical project exposure is provided to simulate real work experience.
Yes. Brolly Ai offers structured placement guidance, resume preparation, mock interviews, and job support.
Companies look for practical knowledge in LLMs, prompt design, automation, and project implementation.
Yes, AI adoption is increasing, making Generative AI skills valuable for long-term growth.
Brolly Ai in offers expert-led training, hands-on LLM projects, placement support, and beginner-friendly guidance for career switchers and non-IT learners.
