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Generative AI
Advance Your Professional Edge with Generative AI
Advance your tech career with Generative AI training led by seasoned industry experts. Our instructors are highly skilled in cutting-edge AI frameworks and have deep hands-on experience building real-world AI solutions used by leading global organizations. With practical labs, real-world AI projects, and step-by-step professional guidance, you’ll gain the expertise needed to master large language models, prompt engineering, neural networks, and AI-driven automation. Start your journey with professionals who work with these technologies every day get certified, get specialized, and move ahead in the world of artificial intelligence.
Bestseller
4.5
- Vendor Neutral
- English
- 4 Months
-
$4000
$8000
Classes are held two hours long and twice a week
- Available Timings (Monday to Friday) 6PM - 10PM
- Available Timings (Saturday) 10AM - 10PM
- Available Timings (Sunday ) 10AM - 4PM
Revolutionize Collaboration with Generative AI
01. Module 1: Introduction to Generative AI
1.1 Overview of Generative AI
Definition and key concepts
Types of generative models (GANs, VAEs, Transformers)
Applications: text, images, audio, code, simulations
Ethical considerations and AI safety
1.2 Core Technologies
Neural networks and deep learning fundamentals
Transformer architecture and attention mechanisms
Large language models (LLMs)
Diffusion models and image generation
1.3 Generative AI Platforms & Tools
OpenAI API, ChatGPT, DALL·E
Google Bard, Vertex AI, PaLM
Hugging Face Models and Transformers
Local deployment frameworks (PyTorch, TensorFlow)
02. Module 2: Planning and Preparing for Generative AI Projects
2.1 Project Scoping & Strategy
Identifying use cases and objectives
Feasibility assessment and ROI considerations
Dataset requirements and sources
2.2 Data Management & Preparation
Data collection and cleaning
Preprocessing for text, images, and multimodal datasets
Data augmentation techniques
2.3 AI Governance & Ethics
AI model bias and fairness
Privacy and data protection regulations
Responsible AI policies
03. Module 3: Generative AI Model Development
3.1 Model Selection & Architecture
Choosing between GANs, VAEs, and Transformers
Fine-tuning vs training from scratch
Model size and compute considerations
3.2 Training & Optimization
Training pipelines and frameworks
Hyperparameter tuning
Evaluation metrics for generative models
3.3 Model Management
Versioning and reproducibility
Model monitoring and lifecycle management
Continuous learning and updates
04. Module 4: Text Generation & Language Models
4.1 LLM Fundamentals
Tokenization and embeddings
Prompt engineering techniques
Fine-tuning LLMs for domain-specific tasks
4.2 Practical Applications
Chatbots and conversational AI
Content generation (articles, code, reports)
Summarization, translation, and Q&A systems
4.3 Policies & Safety
Moderation and safety filters
Handling hallucinations and misinformation
Responsible use of AI-generated content
05. Module 5: Image, Audio, and Multimedia Generation
5.1 Image Generation
Diffusion models and GANs for images
Text-to-image generation and prompts
Style transfer and creative AI tools
5.2 Audio & Music Generation
Speech synthesis and voice cloning
Music composition using AI models
Audio data preprocessing
5.3 Video & Multimodal AI
AI-generated video basics
Combining text, image, and audio for multimedia outputs
Multimodal applications and tools
06. Module 6: Deployment and Integration of Generative AI
6.1 Deployment Strategies
Cloud deployment (Azure, AWS, GCP)
On-premise vs hybrid AI models
APIs and model serving
6.2 Application Integration
Embedding AI in web and mobile apps
Chatbots, assistants, and interactive tools
Automation of business workflows
6.3 Performance Monitoring
Real-time monitoring of AI outputs
User feedback integration
Continuous model improvements
07. Module 7: Advanced Topics in Generative AI
7.1 Reinforcement Learning with AI
RLHF (Reinforcement Learning from Human Feedback)
Reward models and fine-tuning
Applications in alignment and AI safety
7.2 Explainability & Interpretability
Understanding model decisions
Visualization techniques
Trustworthy AI
7.3 Cutting-edge Trends
Foundation models and multimodal AI
Self-supervised learning
Future directions and research areas
08. Module 8: Monitoring, Troubleshooting, and Best Practices
8.1 Model Evaluation & Metrics
Evaluating text, image, and audio outputs
Precision, recall, FID, BLEU, ROUGE metrics
8.2 Troubleshooting AI Systems
Debugging model outputs
Handling low-quality or biased results
Fine-tuning and retraining strategies
8.3 Governance & Compliance
Ethical review of outputs
Regulatory compliance for AI deployment
Best practices for enterprise AI adoption
Professional Pathways
Generative AI Engineer
Proficiency in AI frameworks, programming languages, and data science techniques.
Generative AI Scientist
Strong academic background in AI, deep understanding of machine learning theory, and research experience.
Generative AI Strategist
Business acumen, strategic thinking, and knowledge of generative AI technologies and their potential applications.
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