Best Generative AI Training & Course in Hyderabad
The Generative AI Course is designed to introduce learners to the concepts, technologies, and applications of artificial intelligence that can create content, including text, images, audio, and video. The course covers foundational AI and machine learning principles, neural networks, deep learning models, and advanced generative architectures like GANs and transformers. Participants will gain hands-on experience building and fine-tuning generative AI models, enabling them to create real-world AI solutions for industries such as marketing, entertainment, design, and software development.
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Course Overview
The Generative AI Course provides a comprehensive introduction to the field of artificial intelligence focused on creating new content. Learners will explore the principles of AI, machine learning, neural networks, and deep learning, gaining an understanding of how generative models can produce text, images, audio, and video. The course covers both theoretical foundations and practical applications, making it suitable for beginners and professionals alike.
Through hands-on projects and labs, participants will work with state-of-the-art generative AI models, including GANs, VAEs, and transformer-based architectures like GPT. Learners will gain experience in data preprocessing, model training, fine-tuning, and deployment, developing the skills needed to create AI-generated content for real-world applications across industries such as marketing, entertainment, and software development.
Upon completion, participants will be equipped for careers in AI development, machine learning engineering, and AI-driven content creation. The course also prepares learners for relevant industry-recognized certifications, demonstrating expertise in generative AI and enhancing employability in this fast-growing, high-demand field.
Key Highlights
Comprehensive AI & ML Foundation
Learn core concepts of artificial intelligence, machine learning, and deep learning as the base for generative models.
Hands-On Model Training
Gain practical experience building and fine-tuning models like GANs, VAEs, and transformer-based architectures.
Content Generation Skills
Create AI-generated text, images, audio, and video for real-world applications.
Advanced Generative Techniques
Explore state-of-the-art models, including GPT, diffusion models, and reinforcement learning for generative tasks.
Data Handling & Preprocessing
Learn to manage datasets, clean data, and prepare it for model training efficiently.
Industry-Relevant Applications
Apply generative AI skills in sectors like marketing, gaming, entertainment, design, and software development.
Key Features
Comprehensive Curriculum
Complete journey from ML fundamentals to production-ready GenAI applications
100% Hands-on Training
Practical implementation with real-world coding exercises and projects
Industry-Standard Tools
Learn current frameworks like Transformers, LangChain, CrewAI, and vLLM
Expert-Led Sessions
Learn from practitioners with deep GenAI and LLM expertise
Production-Ready Skills
Master deployment, monitoring, and scaling of LLM applications
Agentic AI Development
Build sophisticated AI agents using cutting-edge frameworks
Capstone Project
End-to-end GenAI application development and deployment
Live Projects & Assignments
Real-world scenarios and industry-relevant problem solving
Career Support
Resume building, mock interviews, and placement assistance
- Foundational AI & Machine Learning Concepts
- Hands-On Training with GANs, VAEs & Transformers
- Multi-Modal Content Generation
- State-of-the-Art Techniques
- Data Preparation & Preprocessing
- Industry-Relevant Applications
- Certification Preparation
- Expert Instructor Guidance
- Lifetime Access to Course Materials
Skills Covered
Machine Learning Fundamentals
Supervised, unsupervised learning, and model evaluation with MLflow
Deep Learning & Neural Networks
CNNs, RNNs, optimization using PyTorch and Hugging Face Transformers
Transformer Architecture
Attention mechanisms, BERT, GPT using Hugging Face ecosystem
Large Language Models
LLM training, scaling, and deployment with vLLM and Ollama
Retrieval-Augmented Generation
Vector databases with Qdrant, embedding models, and LlamaIndex
Agentic AI Development
Multi-agent systems using CrewAI, LlamaIndex, AgentGPT, and LangGraph
Production Deployment
– Model serving with vLLM, LitServe, monitoring with MLflow
Vector Database Management
Qdrant, Pinecone, and similarity search optimization
Framework Mastery
Hugging Face, LangChain, Ollama, and modern GenAI toolstack
Deployment & Integration
Ethics & Responsible AI
Text Generation
Fundamentals of AI & Machine Learning
Generative Model Development
Audio & Video Generation
Data Preprocessing & Handling
Model Evaluation & Optimization
Real-World Project Implementation
Eligibility
Any Degree – B. Tech, BSc, B. Com, BBA, etc
All IT & Non-IT Branches – CSE, EEE, Civil, Mech, Bio, etc.
Coding knowledge is required
No CGPA cut-off. Career gap is not a barrier.
Course Curriculum
Course Overview
This comprehensive GenAI course is designed to take learners from foundational Machine Learning and Deep Learning concepts to advanced Generative AI applications and production deployment. The curriculum covers the complete journey from ML fundamentals through transformer architectures, large language models, retrieval-augmented generation, agentic applications, and real-world productionization. The course follows a structured approach with step-by-step learning modules, ensuring practical implementation skills alongside theoretical understanding.
Prerequisites
- Basic Python programming (include classes understanding)
- Understanding of linear algebra and calculus
- Understanding of Probability and Statistics
Week 1: ML Primer
Session 1: Introduction to Machine Learning
Session 2: Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
Session 3: Data Preprocessing and Feature Engineering
Session 4: Linear and Logistic Regression
Session 5: Support Vector Machines (SVM)
Session 6: Decision Trees and Ensemble Methods
Session 7: Bagging and Boosting Techniques
Session 8: Model Evaluation and Validation
Session 9: Overfitting, Underfitting, and Regularization
Week 2: DL Primer
Session 1: Introduction to Neural Networks
Session 2: Perceptrons and Multi-layer Perceptrons
Session 3: Backpropagation and Gradient Descent
Session 4: Activation Functions and Loss Functions
Session 5: Optimizers (SGD, Adam, RMSprop, etc.)
Session 6: Regularization Techniques and Dropout
Session 7: Recurrent Neural Networks (RNNs) and LSTMs
Session 8: Deep Learning Frameworks and Tools
Week 3: LLM Architecture and Training
Session 1: Introduction to Transformer Architecture
Session 2: Understanding Pre-training vs Fine-tuning
Session 3: Model Scaling Laws and Emergent Abilities
Session 4: Training Data Collection and Preprocessing
Session 5: GPT Family – Architecture and Evolution
Session 6: BERT and Bidirectional Models
Session 7: T5 and Text-to-Text Transfer Transformer
Session 8: LLaMA and Meta’s Language Models
Session 9: Comparing LLM Families and Use Cases
Week 4: LLM Techniques
Session 1: Introduction to Prompt Engineering
Session 2: Prompt Engineering Best Practices
Session 3: In-context Learning and Few-shot Prompting
Session 4: Chain-of-thought Reasoning
Session 5: Zero-shot vs Few-shot vs Many-shot Learning
Session 6: RLHF (Reinforcement Learning from Human Feedback)
Session 7: Model Alignment and Safety Considerations
Session 8: Evaluation Metrics for LLMs
Session 9: Advanced Prompting Techniques and Applications
Week 5: Transformers-1
Session 1: Understanding Vectors and Embeddings
Session 2: Introduction to Attention Mechanism
Session 3: Self-Attention and Multi-Head Attention
Session 4: Transformer Architecture Deep Dive
Week 6: Transformers-2
Session 5: Encoder and Decoder Components
Session 6: Positional Encoding and Embeddings
Session 7: Training Transformers – Loss Functions and Optimization
Week 7: Transformers-3
Session 8: BERT – Bidirectional Encoder Representations
Session 9: GPT – Generative Pre-trained Transformers
Session 10: Fine-tuning and Transfer Learning with Transformers
Week 8: RAG (Retrieval-Augmented Generation)
Session 1: Introduction to RAG and Information Retrieval
Session 2: Vector Databases and Similarity Search
Session 3: Document Chunking and Text Preprocessing
Session 4: Embedding Models for Retrieval
Session 5: RAG Pipeline Architecture
Session 6: Retrieval Strategies and Ranking
Session 7: Generation with Retrieved Context
Session 8: RAG Evaluation and Optimization
Session 9: Advanced RAG Techniques and Applications
Week 9: Productionising LLM Applications
Session 1: Introduction to LLM Production Deployment
Session 2: GPU Infrastructure and Hardware Requirements
Session 3: Model Serving with Transformers and Hugging Face
Session 4: LitServe for High-Performance Model Serving
Session 5: vLLM for Efficient LLM Inference
Session 6: Model Optimization and Quantization Techniques
Session 7: Monitoring LLM Applications and Performance Metrics
Session 8: MLOps for LLM Applications
Week 10: Agentic Applications
Session 1: Introduction to AI Agents and Agentic Workflows
Session 2: Agent Architecture and Planning Systems
Session 3: Agno Framework and Implementation
Session 4: CrewAI for Multi-Agent Collaboration
Session 5: LlamaIndex for Data-Driven Agents
Session 6: LangGraph for Complex Agent Workflows
Session 7: Tool Integration and Function Calling in Agents
Session 8: Memory and State Management in Agents
Session 9: Building and Deploying Production Agent Systems
Bonus Week: Advanced LLM Concepts and Applications
Session 1: Multi-modal LLMs (Vision-Language Models)
Session 2: LLM Agents and Tool Usage
Session 3: Memory Systems and Long Context Handling
Session 4: Model Compression and Efficient Architectures
Session 5: Domain Adaptation and Specialized LLMs
Session 6: LLM Security and Adversarial Attacks
Session 7: Federated Learning with LLMs
Session 8: LLM Interpretability and Explainability
Session 9: Future Trends and Emerging LLM Technologies
FAQ's
What is Generative AI and why is it important?
Generative AI systems can produce new data such as text, images, and code by learning patterns from existing data. It is increasingly important for applications like chatbots, automation, and creative content development.
What career opportunities does this course prepare for?
Completion opens doors to roles such as Machine Learning Engineer, AI Developer, Data Scientist, AI Researcher, and LLM Application Specialist, with practical skills in modern industry-standard tools and frameworks.
Do I need previous AI experience to join?
Applicants are expected to know Python programming and have a fundamental understanding of linear algebra, calculus, probability, and statistics, but extensive prior AI project experience is not required.
Answer: Along with math concepts we should have good understanding of deep learning and machine learning.
How is learning assessed throughout the course?
Assessment includes live coding exercises, capstone projects, assignments, and mock interviews focused on real-world readiness.
What support is available if I struggle with a topic?
Mentors lead sessions and offer personalized guidance. Project-based learning ensures participants receive help with specific challenges.
How are real-world problems incorporated?
Assignments and live projects are designed to tackle industry-relevant problems, building practical problem-solving skills for GenAI applications.
What if I miss a session? Are recordings available?
Most expert-led courses offer recordings or supplementary materials for missed sessions, but this should be confirmed during enrollment.
Can I access the course content after completion?
Access policies may vary. Capstone projects and downloadable resources are generally available for post-course review and reference.
Can working professionals balance this course with a job?
The modular, session-based curriculum is designed for flexibility, allowing professionals to upskill alongside work commitments.
Will there be networking opportunities with industry experts?
Yes, the course includes guest lectures, panel discussions, and community events where participants can connect with AI practitioners and industry leaders.
Who is this GenAI course suitable for?
The course serves both students and professionals seeking hands-on expertise in Machine Learning, Deep Learning, and Generative AI methods using industry tools.
What are the prerequisites for joining?
- Basic Python programming skills (including understanding classes)
- Familiarity with linear algebra, calculus, probability, and statistics
- Moderate to Good Knowledge on the Machine Learning and Deep Learning.
What skills and toolsets are taught?
- Machine Learning fundamentals such as supervised/unsupervised learning, model evaluation,
and neural networks - Advanced transformer models (BERT, GPT, T5, LLaMA) and attention mechanisms
- Use of frameworks like Hugging Face, LangChain, CrewAI, vLLM, Ollama, Qdrant, and Pinecone
- Deployment, monitoring, and scaling of LLM applications in production environments
- Retrieval-augmented generation and agentic AI solutions
How is the course structured?
- 11 weeks in total, including a bonus week
- 89 sessions, progressing from foundational topics to advanced implementation
- Each module focuses on a key area, supported by live projects and capstone assignments
What hands-on experience will participants gain?
- Real-world coding exercises
- Development, evaluation, and deployment of GenAI applications
- Application of multi-agent and retrieval-augmented systems
What career support is provided?
Participants benefit from resume building, mock interviews, and job placement assistance tailored to GenAI career paths.
Which frameworks and libraries are covered?
Hugging Face, LangChain, CrewAI, vLLM, Qdrant, Pinecone, Ollama, LlamaIndex, LitServe, and MLflow, among others
Is certification provided?
While direct certification is not specified, completion includes a capstone project and production-level skills suitable for career advancement.
How are sessions delivered?
Sessions are practitioner-led and feature interactive, step-by-step assignments.
Are advanced topics included?
A bonus week covers multi-modal LLMs, LLM agents and tool usage, security and adversarial attacks, federated learning, interpretability, explainability, and emerging trends in Generative AI.
Generative AI Certification Overview
Upon completing the Generative AI Course, learners will receive a professional certification validating their expertise in building, training, and deploying generative AI models across text, image, audio, and video domains. The certification demonstrates practical skills in GANs, VAEs, transformers, large language models, multi-modal AI, and responsible AI practices.
This credential enhances career prospects for roles such as AI Developer, Machine Learning Engineer, Data Scientist, and AI Content Creator, providing industry-recognized recognition for both technical knowledge and hands-on experience. It also serves as a stepping stone for advanced AI certifications and specialized roles in the rapidly growing field of generative AI.
